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Mousavi H, Dauly G, Dieuset G, El Merhie A, Ismailova E, Wendling F, Al Harrach M. Tuning Microelectrodes' Impedance to Improve Fast Ripples Recording. Bioengineering (Basel) 2024; 11:102. [PMID: 38275582 PMCID: PMC11154299 DOI: 10.3390/bioengineering11010102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/11/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024] Open
Abstract
Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200-600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs' observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers.
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Affiliation(s)
- Hajar Mousavi
- Bioelectronics Department, Ecoles des Mines de Saint Etienne, CMP-EMSE, MOC, 13541 Gardanne, France; (H.M.); (A.E.M.); (E.I.)
| | - Gautier Dauly
- INSERM, LTSI-U1099, University of Rennes, 35000 Rennes, France; (G.D.); (G.D.); (F.W.)
| | - Gabriel Dieuset
- INSERM, LTSI-U1099, University of Rennes, 35000 Rennes, France; (G.D.); (G.D.); (F.W.)
| | - Amira El Merhie
- Bioelectronics Department, Ecoles des Mines de Saint Etienne, CMP-EMSE, MOC, 13541 Gardanne, France; (H.M.); (A.E.M.); (E.I.)
- Laboratoire Matière et Systèmes Complexes, Université Paris Cité, CNRS UMR 7057, 10 Rue Alice Domon et Léonie Duquet, 75013 Paris, France
| | - Esma Ismailova
- Bioelectronics Department, Ecoles des Mines de Saint Etienne, CMP-EMSE, MOC, 13541 Gardanne, France; (H.M.); (A.E.M.); (E.I.)
| | - Fabrice Wendling
- INSERM, LTSI-U1099, University of Rennes, 35000 Rennes, France; (G.D.); (G.D.); (F.W.)
| | - Mariam Al Harrach
- INSERM, LTSI-U1099, University of Rennes, 35000 Rennes, France; (G.D.); (G.D.); (F.W.)
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Zhang X, Yi Y, Cheng L, Chen H, Hu Y. Dynamic effects of miR-20a-5p on hippocampal ripple energy after status epilepticus in rats. Exp Brain Res 2023; 241:2097-2106. [PMID: 37464223 DOI: 10.1007/s00221-023-06663-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/05/2023] [Indexed: 07/20/2023]
Abstract
To determine the dynamic effects of miR-20a-5p on hippocampal ripple energy in rats after status epilepticus (SE). A lithium pilocarpine (LiCl-PILO)-induced rat model of status epilepticus (SE) was established, and the rats were divided into the normal control (Control, CTL), epileptic control (PILO), valproic acid (VPA + PILO), miR-20a-5p overexpression lentivirus vector (miR + PILO), sponges blocking lentivirus vector (Sponges + PILO), and scramble sequence negative control (Scramble + PILO) groups (n = 6). Electroencephalograms (EEGs) were used to analyze changes in hippocampal ripple energy before and after SE. Quantitative polymerase chain reaction (q-PCR) analysis showed that miR-20a-5p levels gradually increased after miR-20a-5p overexpression lentivirus vector injection into the lateral ventricle, and the miR-20a-5p levels were significantly higher than that in CTL group on days 7 and 36 (P < 0.001). The miR-20a-5p levels decreased significantly on days 7 and 36 after blocking by sponges lentivirus vector injected into the lateral ventricle (P < 0.001). After injection of PILO, the average ripple energy expression in each group gradually increased, and reached the peak before chloral hydrate injection (compared with 1 day before SE, P < 0.05). The ripple energy in the VPA + PILO and Sponges + PILO groups was significantly lower than that in the PILO group at 60 min and 70 min after PILO injection and before chloral hydrate injection (P < 0.05), and maintained lower until 2 h after chloral hydrate injection in VPA + PILO (P < 0.05). Compared with the VPA + PILO group, the mean ripple energy of the Sponges + PILO group had no difference at all time points (P ≥ 0.05). After SE, ripple distribution of space and energy is closely related to the occurrence of epilepsy. Inhibition of miR20a-5p expression can downregulate ripple oscillation energy during seizure.
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Affiliation(s)
- Xinyu Zhang
- Department of Neurology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, No.136 Zhongshan 2nd Road, Yu Zhong District, Chongqing, 400014, China
- Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Yanjun Yi
- Department of Neurology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, No.136 Zhongshan 2nd Road, Yu Zhong District, Chongqing, 400014, China
- Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Li Cheng
- Department of Neurology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, No.136 Zhongshan 2nd Road, Yu Zhong District, Chongqing, 400014, China
- Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Hengsheng Chen
- Department of Neurology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, No.136 Zhongshan 2nd Road, Yu Zhong District, Chongqing, 400014, China
- Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China
| | - Yue Hu
- Department of Neurology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, No.136 Zhongshan 2nd Road, Yu Zhong District, Chongqing, 400014, China.
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Barth KJ, Sun J, Chiang CH, Qiao S, Wang C, Rahimpour S, Trumpis M, Duraivel S, Dubey A, Wingel KE, Voinas AE, Ferrentino B, Doyle W, Southwell DG, Haglund MM, Vestal M, Harward SC, Solzbacher F, Devore S, Devinsky O, Friedman D, Pesaran B, Sinha SR, Cogan GB, Blanco J, Viventi J. Flexible, high-resolution cortical arrays with large coverage capture microscale high-frequency oscillations in patients with epilepsy. Epilepsia 2023; 64:1910-1924. [PMID: 37150937 PMCID: PMC10524535 DOI: 10.1111/epi.17642] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023]
Abstract
OBJECTIVE Effective surgical treatment of drug-resistant epilepsy depends on accurate localization of the epileptogenic zone (EZ). High-frequency oscillations (HFOs) are potential biomarkers of the EZ. Previous research has shown that HFOs often occur within submillimeter areas of brain tissue and that the coarse spatial sampling of clinical intracranial electrode arrays may limit the accurate capture of HFO activity. In this study, we sought to characterize microscale HFO activity captured on thin, flexible microelectrocorticographic (μECoG) arrays, which provide high spatial resolution over large cortical surface areas. METHODS We used novel liquid crystal polymer thin-film μECoG arrays (.76-1.72-mm intercontact spacing) to capture HFOs in eight intraoperative recordings from seven patients with epilepsy. We identified ripple (80-250 Hz) and fast ripple (250-600 Hz) HFOs using a common energy thresholding detection algorithm along with two stages of artifact rejection. We visualized microscale subregions of HFO activity using spatial maps of HFO rate, signal-to-noise ratio, and mean peak frequency. We quantified the spatial extent of HFO events by measuring covariance between detected HFOs and surrounding activity. We also compared HFO detection rates on microcontacts to simulated macrocontacts by spatially averaging data. RESULTS We found visually delineable subregions of elevated HFO activity within each μECoG recording. Forty-seven percent of HFOs occurred on single 200-μm-diameter recording contacts, with minimal high-frequency activity on surrounding contacts. Other HFO events occurred across multiple contacts simultaneously, with covarying activity most often limited to a .95-mm radius. Through spatial averaging, we estimated that macrocontacts with 2-3-mm diameter would only capture 44% of the HFOs detected in our μECoG recordings. SIGNIFICANCE These results demonstrate that thin-film microcontact surface arrays with both highresolution and large coverage accurately capture microscale HFO activity and may improve the utility of HFOs to localize the EZ for treatment of drug-resistant epilepsy.
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Affiliation(s)
- Katrina J. Barth
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - James Sun
- Center for Neural Science, New York University, New York, NY, USA
| | - Chia-Han Chiang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shaoyu Qiao
- Center for Neural Science, New York University, New York, NY, USA
| | - Charles Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Shervin Rahimpour
- Department of Neurosurgery, Clinical Neuroscience Center, University of Utah, Salt Lake City, UT, USA
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Michael Trumpis
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Agrita Dubey
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katie E. Wingel
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alex E. Voinas
- Center for Neural Science, New York University, New York, NY, USA
| | | | - Werner Doyle
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
| | - Derek G. Southwell
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Michael M. Haglund
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Matthew Vestal
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Stephen C. Harward
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Florian Solzbacher
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT, USA
| | - Sasha Devore
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Orrin Devinsky
- Department of Neurosurgery, NYU Langone Medical Center, New York City, NY, USA
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
- Comprehensive Epilepsy Center, NYU Langone Health, New York, NY, USA
| | - Daniel Friedman
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, USA
| | - Bijan Pesaran
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saurabh R. Sinha
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gregory B. Cogan
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Center for Cognitive Neuroscience, Duke University, Durham, NC, USA
| | - Justin Blanco
- Department of Electrical and Computer Engineering, United States Naval Academy, Annapolis, MD, USA
| | - Jonathan Viventi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke School of Medicine, Durham, NC, USA
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
- Duke Comprehensive Epilepsy Center, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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Lisgaras CP, Scharfman HE. High-frequency oscillations (250-500 Hz) in animal models of Alzheimer's disease and two animal models of epilepsy. Epilepsia 2023; 64:231-246. [PMID: 36346209 PMCID: PMC10501735 DOI: 10.1111/epi.17462] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE To test the hypothesis that high-frequency oscillations (HFOs) between 250 and 500 Hz occur in mouse models of Alzheimer's disease (AD) and thus are not unique to epilepsy. METHODS Experiments were conducted in three mouse models of AD: Tg2576 mice that simulate a form of familial AD, presenilin 2 knock-out (PS2KO) mice, and the Ts65Dn model of Down's syndrome. We recorded HFOs using wideband (0.1-500 Hz, 2 kHz) intra-hippocampal and cortical surface electroencephalography (EEG) at 1 month until 24 months of age during wakefulness, slow wave sleep (SWS), and rapid eye movement (REM) sleep. In addition, interictal spikes (IISs) and seizures were analyzed for the possible presence of HFOs. Comparisons were made to the intra-hippocampal kainic acid and pilocarpine models of epilepsy. RESULTS We describe for the first time that hippocampal and cortical HFOs are a new EEG abnormality in AD mouse models. HFOs occurred in all transgenic mice but no controls. They were also detectable as early as 1 month of age and prior to amyloid beta plaque neuropathology. HFOs were most frequent during SWS (vs REM sleep or wakefulness). Notably, HFOs in the AD and epilepsy models were indistinguishable in both spectral frequency and duration. HFOs also occurred during IISs and seizures in the AD models, although with altered spectral properties compared to isolated HFOs. SIGNIFICANCE Our data demonstrate that HFOs, an epilepsy biomarker with high translational value, are not unique to epilepsy and thus not disease specific. Our findings also strengthen the idea of hyperexcitability in AD and its significant overlap with epilepsy. HFOs in AD mouse models may serve as an EEG biomarker, which is detectable from the scalp and thus amenable to noninvasive detection in people at risk for AD.
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Affiliation(s)
- Christos Panagiotis Lisgaras
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016
- Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962
| | - Helen E. Scharfman
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016
- Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962
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Zhang Y, Chung H, Ngo JP, Monsoor T, Hussain SA, Matsumoto JH, Walshaw PD, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Characterizing physiological high-frequency oscillations using deep learning. J Neural Eng 2022; 19:10.1088/1741-2552/aca4fa. [PMID: 36541546 PMCID: PMC10364130 DOI: 10.1088/1741-2552/aca4fa] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 11/22/2022] [Indexed: 11/23/2022]
Abstract
Objective.Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning (DL).Approach.We studied children with neocortical epilepsy who underwent intracranial strip/grid evaluation. Time-series EEG data were transformed into DL training inputs. The eloquent cortex (EC) was defined by functional cortical mapping and used as a DL label. Morphological characteristics of HFOs obtained from EC (ecHFOs) were distilled and interpreted through a novel weakly supervised DL model.Main results.A total of 63 379 interictal intracranially-recorded HFOs from 18 children were analyzed. The ecHFOs had lower amplitude throughout the 80-500 Hz frequency band around the HFO onset and also had a lower signal amplitude in the low frequency band throughout a one-second time window than non-ecHFOs, resembling a bell-shaped template in the time-frequency map. A minority of ecHFOs were HFOs with spikes (22.9%). Such morphological characteristics were confirmed to influence DL model prediction via perturbation analyses. Using the resection ratio (removed HFOs/detected HFOs) of non-ecHFOs, the prediction of postoperative seizure outcomes improved compared to using uncorrected HFOs (area under the ROC curve of 0.82, increased from 0.76).Significance.We characterized salient features of physiological HFOs using a DL algorithm. Our results suggested that this DL-based HFO classification, once trained, might help separate physiological from pathological HFOs, and efficiently guide surgical resection using HFOs.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hoyoung Chung
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Jacquline P. Ngo
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Patricia D. Walshaw
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
- Department of Neurobiology, University of California, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
- The Brain Research Institute, University of California, Los Angeles, CA, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
- Department of Bioengineering, University of California, Los Angeles, CA, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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Localization of epileptogenic foci by automatic detection of high‐frequency oscillations based on waveform feature templates. INT J INTELL SYST 2022. [DOI: 10.1002/int.23052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Zhou Y, You J, Kumar U, Weiss SA, Bragin A, Engel J, Papadelis C, Li L. An approach for reliably identifying high-frequency oscillations and reducing false-positive detections. Epilepsia Open 2022; 7:674-686. [PMID: 36053171 PMCID: PMC9712470 DOI: 10.1002/epi4.12647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 08/31/2022] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE Aiming to improve the feasibility and reliability of using high-frequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. METHODS We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a time-frequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation. RESULTS The algorithm was run on 768-h recordings of intracranial electrodes in 48 PTE animals. A total of 453 917 HFOs were identified by initial HFO detection, of which 450 917 were implemented for HFO refinement and 203 531 events were retained. Random sampling was used to evaluate the performance of the detector. The HFO detection yielded an overall accuracy of 0.95 ± 0.03 , with precision, recall, and F1 scores of 0.92 ± 0.05 , 0.99 ± 0.01 , and 0.94 ± 0.03 , respectively. For the HFO classification, our algorithm obtained an accuracy of 0.97 ± 0.02 . For the inter-rater reliability of algorithm evaluation, the agreement among four experts was 0.94 ± 0.03 for HFO detection and 0.85 ± 0.04 for HFO classification. SIGNIFICANCE Our approach shows that a segregated pipeline design with a focus on false-positive rejection can improve the detection efficiency and provide reliable results. This pipeline does not require customization and uses fixed parameters, making it highly feasible and translatable for basic and clinical applications of epilepsy.
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Affiliation(s)
- Yufeng Zhou
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Jing You
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA
| | - Udaya Kumar
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Shennan A Weiss
- Departments of Neurology, Department of Physiology and PharmacologyState University of New York DownstateBrooklynNew YorkUSA,Department of NeurologyNew York City Health + Hospitals/Kings CountyBrooklynNew YorkUSA
| | - Anatol Bragin
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Jerome Engel
- Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA,Brain Research InstituteUniversity of CaliforniaLos AngelesCaliforniaUSA,Department of NeurobiologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA,Department of Psychiatry and Biobehavioral SciencesDavid Geffen School of Medicine at UCLACaliforniaUSA
| | - Christos Papadelis
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexasUSA,School of MedicineTexas Christian UniversityFort WorthTexasUSA,Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexasUSA
| | - Lin Li
- Department of Biomedical EngineeringUniversity of North TexasTexasUSA,Department of NeurologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
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8
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Besheli BF, Sha Z, Gavvala JR, Gurses C, Karamursel S, Quach MM, Curry DJ, Sheth SA, Francis DJ, Henry TR, Ince NF. A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization. J Neural Eng 2022; 19:10.1088/1741-2552/ac8766. [PMID: 35931045 PMCID: PMC9901915 DOI: 10.1088/1741-2552/ac8766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 08/05/2022] [Indexed: 02/08/2023]
Abstract
Objective.High-frequency oscillations (HFOs) are considered a biomarker of the epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors confound true oscillations with spurious events caused by the presence of artifacts.Approach.We hypothesized that, unlike pseudo-HFOs with sharp transients or arbitrary shapes, real HFOs have a signal characteristic that can be represented using a small number of oscillatory bases. Based on this hypothesis using a sparse representation framework, this study introduces a new classification approach to distinguish true HFOs from the pseudo-events that mislead seizure onset zone (SOZ) localization. Moreover, we further classified the HFOs into ripples and fast ripples by introducing an adaptive reconstruction scheme using sparse representation. By visualizing the raw waveforms and time-frequency representation of events recorded from 16 patients, three experts labeled 6400 candidate events that passed an initial amplitude-threshold-based HFO detector. We formed a redundant analytical multiscale dictionary built from smooth oscillatory Gabor atoms and represented each event with orthogonal matching pursuit by using a small number of dictionary elements. We used the approximation error and residual signal at each iteration to extract features that can distinguish the HFOs from any type of artifact regardless of their corresponding source. We validated our model on sixteen subjects with thirty minutes of continuous interictal intracranial EEG recording from each.Main results.We showed that the accuracy of SOZ detection after applying our method was significantly improved. In particular, we achieved a 96.65% classification accuracy in labeled events and a 17.57% improvement in SOZ detection on continuous data. Our sparse representation framework can also distinguish between ripples and fast ripples.Significance.We show that by using a sparse representation approach we can remove the pseudo-HFOs from the pool of events and improve the reliability of detected HFOs in large data sets and minimize manual artifact elimination.
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Affiliation(s)
| | - Zhiyi Sha
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - Jay R. Gavvala
- Department of Neurology-Neurophysiology, Baylor College of Medicine, Houston, TX, USA
| | - Candan Gurses
- Department of Neurology, School of Medicine, Koç Üniversitesi, Istanbul, Turkey
| | - Sacit Karamursel
- Department of Physiology, School of Medicine, Koç Üniversitesi, Istanbul, Turkey
| | - Michael M. Quach
- Department of Neurology, Texas Children’s Hospital, Houston, Texas, USA
| | - Daniel J. Curry
- Department of Neurosurgery, Texas Children’s Hospital, Houston, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - David J. Francis
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Thomas R. Henry
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - Nuri F. Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
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9
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Xie T, Wu Z, Schalk G, Tong Y, Vato A, Raviv N, Guo Q, Ye H, Sheng X, Zhu X, Brunner P, Chen L. Automated intraoperative central sulcus localization and somatotopic mapping using median nerve stimulation. J Neural Eng 2022; 19. [PMID: 35785769 PMCID: PMC9534515 DOI: 10.1088/1741-2552/ac7dfd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 07/04/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Accurate identification of functional cortical regions is essential in neurological resection. The central sulcus (CS) is an important landmark that delineates functional cortical regions. Median nerve stimulation (MNS) is a standard procedure to identify the position of the CS intraoperatively. In this paper, we introduce an automated procedure that uses MNS to rapidly localize the CS and create functional somatotopic maps. APPROACH We recorded electrocorticographic signals from 13 patients who underwent MNS in the course of an awake craniotomy. We analyzed these signals to develop an automated procedure that determines the location of the CS and that also produces functional somatotopic maps. MAIN RESULTS The comparison between our automated method and visual inspection performed by the neurosurgeon shows that our procedure has a high sensitivity (89%) in identifying the CS. Further, we found substantial concordance between the functional somatotopic maps generated by our method and passive functional mapping (92% sensitivity). SIGNIFICANCE Our automated MNS-based method can rapidly localize the CS and create functional somatotopic maps without imposing additional burden on the clinical procedure. With additional development and validation, our method may lead to a diagnostic tool that guides neurosurgeon and reduces postoperative morbidity in patients undergoing resective brain surgery.
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Affiliation(s)
- Tao Xie
- Department of Neurosurgery, Washington University School of Medicine in Saint Louis, 660 S. Euclid Avenue, St Louis, Missouri, 63110-1010, UNITED STATES
| | - Zehan Wu
- Dept. of Neurosurgery, Huashan Hospital Fudan University, 12 Wulumuqi Middle Rd, Shanghai, 200040, CHINA
| | - Gerwin Schalk
- National Center for Adaptive Neurotechnologies, 113 Holland Avenue, Albany, New York, 12208, UNITED STATES
| | - Yusheng Tong
- Dept. of Neurosurgery, Huashan Hospital Fudan University, 12 Wulumuqi Middle Rd, Shanghai, 200040, CHINA
| | - Alessandro Vato
- National Center for Adaptive Neurotechnologies, 113 Holland Avenue, Albany, New York, 12208, UNITED STATES
| | - Nataly Raviv
- National Center for Adaptive Neurotechnologies, 113 Holland Avenue, Albany, New York, 12208, UNITED STATES
| | - Qinglong Guo
- Dept. of Neurosurgery, Huashan Hospital Fudan University, 12 Wulumuqi Middle Rd, Shanghai, 200040, CHINA
| | - Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, CHINA
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, CHINA
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration , Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, CHINA
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine in Saint Louis, 660 S. Euclid Avenue, St Louis, Missouri, 63110-1010, UNITED STATES
| | - Liang Chen
- Dept. of Neurosurgery, Huashan Hospital Fudan University, 12 Wulumuqi Middle Rd, Shanghai, 200040, CHINA
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10
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Tamrakar S, Iimura Y, Suzuki H, Mitsuhashi T, Ueda T, Nishioka K, Karagiozov K, Nakajima M, Miao Y, Tanaka T, Sugano H. Higher phase-amplitude coupling between ripple and slow oscillations indicates the distribution of epileptogenicity in temporal lobe epilepsy with hippocampal sclerosis. Seizure 2022; 100:1-7. [DOI: 10.1016/j.seizure.2022.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 10/18/2022] Open
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11
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Lisgaras CP, Scharfman HE. Robust chronic convulsive seizures, high frequency oscillations, and human seizure onset patterns in an intrahippocampal kainic acid model in mice. Neurobiol Dis 2022; 166:105637. [PMID: 35091040 PMCID: PMC9034729 DOI: 10.1016/j.nbd.2022.105637] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 01/05/2022] [Accepted: 01/22/2022] [Indexed: 01/21/2023] Open
Abstract
Intrahippocampal kainic acid (IHKA) has been widely implemented to simulate temporal lobe epilepsy (TLE), but evidence of robust seizures is usually limited. To resolve this problem, we slightly modified previous methods and show robust seizures are common and frequent in both male and female mice. We employed continuous wideband video-EEG monitoring from 4 recording sites to best demonstrate the seizures. We found many more convulsive seizures than most studies have reported. Mortality was low. Analysis of convulsive seizures at 2-4 and 10-12 wks post-IHKA showed a robust frequency (2-4 per day on average) and duration (typically 20-30 s) at each time. Comparison of the two timepoints showed that seizure burden became more severe in approximately 50% of the animals. We show that almost all convulsive seizures could be characterized as either low-voltage fast or hypersynchronous onset seizures, which has not been reported in a mouse model of epilepsy and is important because these seizure types are found in humans. In addition, we report that high frequency oscillations (>250 Hz) occur, resembling findings from IHKA in rats and TLE patients. Pathology in the hippocampus at the site of IHKA injection was similar to mesial temporal lobe sclerosis and reduced contralaterally. In summary, our methods produce a model of TLE in mice with robust convulsive seizures, and there is variable progression. HFOs are robust also, and seizures have onset patterns and pathology like human TLE. SIGNIFICANCE: Although the IHKA model has been widely used in mice for epilepsy research, there is variation in outcomes, with many studies showing few robust seizures long-term, especially convulsive seizures. We present an implementation of the IHKA model with frequent convulsive seizures that are robust, meaning they are >10 s and associated with complex high frequency rhythmic activity recorded from 2 hippocampal and 2 cortical sites. Seizure onset patterns usually matched the low-voltage fast and hypersynchronous seizures in TLE. Importantly, there is low mortality, and both sexes can be used. We believe our results will advance the ability to use the IHKA model of TLE in mice. The results also have important implications for our understanding of HFOs, progression, and other topics of broad interest to the epilepsy research community. Finally, the results have implications for preclinical drug screening because seizure frequency increased in approximately half of the mice after a 6 wk interval, suggesting that the typical 2 wk period for monitoring seizure frequency is insufficient.
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Affiliation(s)
- Christos Panagiotis Lisgaras
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016, United States of America,Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962, United States of America
| | - Helen E. Scharfman
- Departments of Child & Adolescent Psychiatry, Neuroscience & Physiology, and Psychiatry, and the Neuroscience Institute, New York University Langone Health, 550 First Ave., New York, NY 10016, United States of America,Center for Dementia Research, The Nathan Kline Institute for Psychiatric Research, New York State Office of Mental Health, 140 Old Orangeburg Road, Bldg. 35, Orangeburg, NY 10962, United States of America,Corresponding author at: The Nathan Kline Institute, Center for Dementia Research, 140 Old Orangeburg Rd. Bldg. 35, Orangeburg, NY 10962, United States of America. (H.E. Scharfman)
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12
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Numata-Uematsu Y, Uematsu M, Sakuraba R, Iwasaki M, Osawa S, Jin K, Nakasato N, Kure S. The Onset of Interictal Spike-Related Ripples Facilitates Detection of the Epileptogenic Zone. Front Neurol 2021; 12:724417. [PMID: 34803874 PMCID: PMC8599368 DOI: 10.3389/fneur.2021.724417] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Accurate estimation of the epileptogenic zone (EZ) is essential for favorable outcomes in epilepsy surgery. Conventional ictal electrocorticography (ECoG) onset is generally used to detect the EZ but is insufficient in achieving seizure-free outcomes. By contrast, high-frequency oscillations (HFOs) could be useful markers of the EZ. Hence, we aimed to detect the EZ using interictal spikes and investigated whether the onset area of interictal spike-related HFOs was within the EZ. Methods: The EZ is considered to be included in the resection area among patients with seizure-free outcomes after surgery. Using a complex demodulation technique, we developed a method to determine the onset channels of interictal spike-related ripples (HFOs of 80-200 Hz) and investigated whether they are within the resection area. Results: We retrospectively examined 12 serial patients who achieved seizure-free status after focal resection surgery. Using the method that we developed, we determined the onset channels of interictal spike-related ripples and found that for all 12 patients, they were among the resection channels. The onset frequencies of ripples were in the range of 80-150 Hz. However, the ictal onset channels (evaluated based on ictal ECoG patterns) and ripple onset channels coincided in only 3 of 12 patients. Conclusions: Determining the onset area of interictal spike-related ripples could facilitate EZ estimation. This simple method that utilizes interictal ECoG may aid in preoperative evaluation and improve epilepsy surgery outcomes.
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Affiliation(s)
| | - Mitsugu Uematsu
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
| | - Rie Sakuraba
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Masaki Iwasaki
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan.,Department of Neurosurgery, National Center Hospital of Neurology and Psychiatry, Tokyo, Japan
| | - Shinichiro Osawa
- Department of Neurosurgery, Tohoku University School of Medicine, Sendai, Japan
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Nobukazu Nakasato
- Department of Epileptology, Tohoku University School of Medicine, Sendai, Japan
| | - Shigeo Kure
- Department of Pediatrics, Tohoku University School of Medicine, Sendai, Japan
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13
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Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun 2021; 4:fcab267. [PMID: 35169696 PMCID: PMC8833577 DOI: 10.1093/braincomms/fcab267] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022] Open
Abstract
Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The ‘purification power’ of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80–250 Hz) and fast ripple (250–500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model’s decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Qiujing Lu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Shaun A. Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Joe X. Qiao
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Noriko Salamon
- Division of Neuroradiology, Department of Radiology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Myung Shin Sim
- Department of Medicine, Statistics Core, University of California, Los Angeles, CA 90095, USA
| | - Eishi Asano
- Department of Pediatrics and Neurology, Children’s Hospital of Michigan, Wayne State University School of Medicine, Detroit, MI 48201, USA
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
| | - Richard J. Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- Department of Neurobiology, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, USA
- The Brain Research Institute, University of California, Los Angeles, CA 90095, USA
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA 90095, USA
- The UCLA Children’s Discovery and Innovation Institute, Los Angeles, CA, USA
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14
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Wu M, Qin H, Wan X, Du Y. HFO Detection in Epilepsy: A Stacked Denoising Autoencoder and Sample Weight Adjusting Factors-Based Method. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1965-1976. [PMID: 34529568 DOI: 10.1109/tnsre.2021.3113293] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
High-frequency oscillations (HFOs) recorded by the intracranial electroencephalography (iEEG) are the promising biomarkers of epileptogenic zones. Accurate detection of HFOs is the key to pre-operative assessment for epilepsy. Due to the subjective bias caused by manual features and the class imbalance between HFOs and false HFOs, it is difficult to obtain satisfactory detection performance by the existing methods. To solve these problems, we put forward a novel method to accurately detect HFOs based on the stacked denoising autoencoder (SDAE) and the ensemble classifier with sample weight adjusting factors. First, the adjustable threshold of Hilbert envelopes is proposed to isolate the events of interest (EoIs) from background activities. Then, the SDAE network is utilized to automatically extract features of EoIs in the time-frequency domain. Finally, the AdaBoost-based support vector machine ensemble classifier with sample weight adjusting factors is devised to separate HFOs from EoIs by using the extracted features. These adjusting factors are used to solve the class imbalance problem by adjusting sample weights when learning the base classifiers. Our HFO detection method is evaluated by using clinical iEEG data recorded from 20 patients with medically refractory epilepsy. The experimental results show that our detection method outperforms some existing methods in terms of sensitivity and false discovery rate. In addition, the HFOs detected by our method are effective for localizing seizure onset zones.
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15
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Yi YJ, Ran X, Xiang J, Li XY, Jiang L, Chen HS, Hu Y. Effect of gap junction blockers on hippocampal ripple energy expression in rats with status epilepticus. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2021; 23:848-853. [PMID: 34511176 DOI: 10.7499/j.issn.1008-8830.2103162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
OBJECTIVES To study the effect of gap junction blockers, quinine (QUIN) and carbenoxolone (CBX), on hippocampal ripple energy expression in rats with status epilepticus (SE). METHODS A total of 24 rats were randomly divided into four groups: model, QUIN, valproic acid (VPA), and CBX (n=6 each). A rat model of SE induced by lithium-pilocarpine (PILO) was prepared. The QUIN, VPA, and CBX groups were given intraperitoneal injection of QUIN (50 mg/kg), VPA by gavage (200 mg/kg), and intraperitoneal injection of CBX (50 mg/kg) respectively, at 3 days before PILO injection. Electroencephalography was used to analyze the change in hippocampal ripple energy before and after modeling, as well as before and after chloral hydrate injection to control seizures. RESULTS Ripple expression was observed in the hippocampal CA1, CA3, and dentate gyrus regions of normal rats. After 10 minutes of PILO injection, all groups had a gradual increase in mean ripple energy expression compared with 1 day before modeling, with the highest expression level before chloral hydrate injection in the model, VPA and CBX groups (P<0.05). The QUIN group had the highest expression level of mean ripple energy 60 minutes after PILO injection. The mean ripple energy returned to normal levels in the three intervention groups immediately after chloral hydrate injection, while in the model group, the mean ripple energy returned to normal levels 1 hour after chloral hydrate injection. The mean ripple energy remained normal till to day 3 after SE in the four groups. The changing trend of maximum ripple energy was similar to that of mean ripple energy. CONCLUSIONS The change in ripple energy can be used as a quantitative indicator for early warning of seizures, while it cannot predict seizures in the interictal period. Gap junction blockers can reduce ripple energy during seizures.
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Affiliation(s)
- Yan-Jun Yi
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing 400136
| | - Xiao Ran
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing 400136
| | | | | | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing 400136
| | - Heng-Sheng Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing 400136
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing 400136
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16
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Nadalin JK, Eden UT, Han X, Richardson RM, Chu CJ, Kramer MA. Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram. J Neurosci Methods 2021; 360:109239. [PMID: 34090917 DOI: 10.1016/j.jneumeth.2021.109239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 05/17/2021] [Accepted: 05/30/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND A reliable biomarker to identify cortical tissue responsible for generating epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike ripple events are a promising biomarker for epileptogenic tissue that currently require expert review for accurate identification. This expert review is time consuming and subjective, limiting reproducibility and high-throughput applications. NEW METHOD To address this limitation, we develop a fully-automated method for spike ripple detection. The method consists of a convolutional neural network trained to compute the probability that a spectrogram image contains a spike ripple. RESULTS We validate the proposed spike ripple detector on expert-labeled data and show that this detector accurately separates subjects with low and high seizure risks. COMPARISON WITH EXISTING METHOD The proposed method performs as well as existing methods that require manual validation of candidate spike ripple events. The introduction of a fully automated method reduces subjectivity and increases rigor and reproducibility of this epilepsy biomarker. CONCLUSION We introduce and validate a fully-automated spike ripple detector to support utilization of this epilepsy biomarker in clinical and translational work.
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Affiliation(s)
- Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States
| | - Xue Han
- Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States; Department of Biomedical Engineering, Boston University, Boston, MA 02215, United States
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States; Center for Systems Neuroscience, Boston University, Boston, MA 02215, United States.
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17
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Wong SM, Arski ON, Workewych AM, Donner E, Ochi A, Otsubo H, Snead OC, Ibrahim GM. Detection of high-frequency oscillations in electroencephalography: A scoping review and an adaptable open-source framework. Seizure 2020; 84:23-33. [PMID: 33271473 DOI: 10.1016/j.seizure.2020.11.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 11/19/2022] Open
Abstract
PURPOSE High frequency oscillations (HFOs) are putative biomarkers of epileptogenicity. These electrophysiological phenomena can be effectively detected in electroencephalography using automated methods. Nonetheless, the implementation of these methods into clinical practice remains challenging as significant variability exists between algorithms and their characterizations of HFOs. Here, we perform a scoping review of the literature pertaining to automated HFO detection methods. In addition, we propose a framework for defining and detecting HFOs based on a simplified single-stage time-frequency based detection algorithm with clinically-familiar parameters. METHODS Several databases (OVID Medline, Web of Science, PubMed) were searched for articles presenting novel, automated HFO detection methods. Details related to the algorithm and various stages of data acquisition, pre-processing, and analysis were abstracted from included studies. RESULTS From the 261 records screened, 57 articles presented novel, automated HFO detection methods and were included in the scoping review. These algorithms were categorized into 3 groups based on their most salient features: energy thresholding, time-frequency analysis, and data mining/machine learning. Algorithms were optimized for specific datasets and suffered from low specificity. A framework for user-constrained inputs is proposed to circumvent some of the weaknesses of highly performant detectors. CONCLUSIONS Further efforts are required to optimize and validate existing automated HFO detection methods for clinical utility. The proposed framework may be applied to understand and standardize the variations in HFO definitions across institutions.
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Affiliation(s)
- Simeon M Wong
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Olivia N Arski
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada
| | - Adriana M Workewych
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Elizabeth Donner
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - Ayako Ochi
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - Hiroshi Otsubo
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - O Carter Snead
- Division of Neurology, Hospital for Sick Children, Toronto, Canada
| | - George M Ibrahim
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada; Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, Canada.
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18
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Thomschewski A, Gerner N, Langthaler PB, Trinka E, Bathke AC, Fell J, Höller Y. Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe. Front Neurol 2020; 11:563577. [PMID: 33192999 PMCID: PMC7604344 DOI: 10.3389/fneur.2020.563577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 08/26/2020] [Indexed: 12/14/2022] Open
Abstract
Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe. Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials. Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined "clear" ripples. Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these "false" HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts.
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Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria,Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria,Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria,*Correspondence: Aljoscha Thomschewski
| | - Nathalie Gerner
- Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Patrick B. Langthaler
- Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria,Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
| | - Arne C. Bathke
- Department of Mathematics, Paris-Lodron University of Salzburg, Salzburg, Austria,Intelligent Data Analytics Lab Salzburg, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Jürgen Fell
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
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19
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Remakanthakurup Sindhu K, Staba R, Lopour BA. Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy. Epilepsia 2020; 61:1553-1569. [PMID: 32729943 DOI: 10.1111/epi.16622] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/17/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022]
Abstract
High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.
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Affiliation(s)
| | | | - Beth A Lopour
- Biomedical Engineering, UC Irvine, Irvine, California, USA
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20
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Qi L, Fan X, Tao X, Chai Q, Zhang K, Meng F, Hu W, Sang L, Yang X, Qiao H. Identifying the Epileptogenic Zone With the Relative Strength of High-Frequency Oscillation: A Stereoelectroencephalography Study. Front Hum Neurosci 2020; 14:186. [PMID: 32581741 PMCID: PMC7296092 DOI: 10.3389/fnhum.2020.00186] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 04/27/2020] [Indexed: 11/16/2022] Open
Abstract
Background High-frequency oscillation (HFO) represents a promising biomarker of epileptogenicity. However, the significant interindividual differences among patients limit its application in clinical practice. Here, we applied and evaluated an individualized, frequency-based approach of HFO analysis in stereoelectroencephalography (SEEG) data for localizing the epileptogenic zones (EZs). Methods Clinical and SEEG data of 19 patients with drug-resistant focal epilepsy were retrospectively analyzed. The individualized spectral power of all signals recorded by electrode array, i.e., the relative strength of HFO, was computed with a wavelet method for each patient. Subsequently, the clinical value of the relative strength of HFO for identifying the EZ was evaluated. Results Focal increase in the relative strength of HFO in SEEG recordings were identified in all 19 patients. HFOs identified inside the clinically identified seizure onset zone had more spectral power than those identified outside (p < 0.001), and HFOs in 250–500 Hz band (fast ripples) seemed to be more specific identifying the EZ than in those in 80–250 Hz band (ripples) (p < 0.01). The resection of brain regions generating HFOs resulted in a favorable seizure outcome in 17 patients (17/19; 89.5%), while in the cases of other patients with poor outcomes, the brain regions generating HFOs were not removed completely. Conclusion The relative strength of HFO, especially fast ripples, is a promising effective biomarker for identifying the EZ and can lead to a favorable seizure outcome if used to guide epilepsy surgery.
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Affiliation(s)
- Lei Qi
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Fengtai Hospital, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaorong Tao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qi Chai
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin Sang
- Beijing Fengtai Hospital, Beijing, China
| | | | - Hui Qiao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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21
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Zhao B, Hu W, Zhang C, Wang X, Wang Y, Liu C, Mo J, Yang X, Sang L, Ma Y, Shao X, Zhang K, Zhang J. Integrated Automatic Detection, Classification and Imaging of High Frequency Oscillations With Stereoelectroencephalography. Front Neurosci 2020; 14:546. [PMID: 32581688 PMCID: PMC7287040 DOI: 10.3389/fnins.2020.00546] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/04/2020] [Indexed: 11/13/2022] Open
Abstract
Objective During presurgical evaluation for focal epilepsy patients, the evidence supporting the use of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) increased over the past decade. This study aims to develop and validate an integrated automatic detection, classification and imaging pipeline of HFOs with stereoelectroencephalography (SEEG) to narrow the gap between HFOs quantitative analysis and clinical application. Methods The proposed pipeline includes stages of channel inclusion, candidate HFOs detection and automatic labeling with four trained convolutional neural network (CNN) classifiers and HFOs sorting based on occurrence rate and imaging. We first evaluated the initial detector using an open simulated dataset. After that, we validated our full algorithm in a 20-patient cohort against three assumptions based on previous studies. Classified HFOs results were compared with seizure onset zone (SOZ) channels for their concordance. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were calculated representing the prediction ability of the labeled HFOs outputs for SOZ. Results The initial detector demonstrated satisfactory performance on the simulated dataset. The four CNN classifiers converged quickly during training, and the accuracies on the validation dataset were above 95%. The localization value of HFOs was significantly improved by HFOs classification. The AUC values of the 20 testing patients increased after HFO classification, indicating a satisfactory prediction value of the proposed algorithm for EZ identification. Conclusion Our detector can provide robust HFOs analysis results revealing EZ at the individual level, which may ultimately push forward the transitioning of HFOs analysis into a meaningful part of the presurgical evaluation and surgical planning.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Yang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Yanshan Ma
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
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22
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Inoue T, Kobayashi K, Matsumoto R, Inouchi M, Togo M, Togawa J, Usami K, Shimotake A, Matsuhashi M, Kikuchi T, Yoshida K, Kawawaki H, Sawamoto N, Kunieda T, Miyamoto S, Takahashi R, Ikeda A. Engagement of cortico-cortical and cortico-subcortical networks in a patient with epileptic spasms: An integrated neurophysiological study. Clin Neurophysiol 2020; 131:2255-2264. [PMID: 32736326 DOI: 10.1016/j.clinph.2020.04.167] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 03/22/2020] [Accepted: 04/13/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE We aimed to delineate the engagement of cortico-cortical and cortico-subcortical networks in the generation of epileptic spasms (ES) using integrated neurophysiological techniques. METHODS Seventeen-year-old male patient with intractable ES underwent chronic subdural electrode implantation for presurgical evaluation. Networks were evaluated in ictal periods using high-frequency oscillation (HFO) analysis and in interictal periods using magnetoencephalography (MEG) and simultaneous electroencephalography, and functional magnetic resonance imaging (EEG-fMRI). Cortico-cortical evoked potentials (CCEPs) were recorded to trace connections among the networks. RESULTS Ictal HFO revealed a network comprising multilobar cortical regions (frontal, parietal, and temporal), but sparing the positive motor area. Interictally, MEG and EEG-fMRI revealed spike-and-wave-related activation in these cortical regions. Analysis of CCEPs provided evidence of connectivity within the cortico-cortical network. Additionally, EEG-fMRI results indicate the involvement of subcortical structures, such as bilateral thalamus (predominantly right) and midbrain. CONCLUSIONS In this case study, integrated neurophysiological techniques provided converging evidence for the involvement of a cortico-cortical network (sparing the positive motor area) and a cortico-subcortical network in the generation of ES in the patient. SIGNIFICANCE Cortico-cortical and cortico-subcortical pathways, with the exception of the direct descending corticospinal pathway from the positive motor area, may play important roles in the generation of ES.
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Affiliation(s)
- Takeshi Inoue
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan; Department of Pediatric Neurology, Child and Adolescent Epilepsy Center, Osaka City General Hospital, 2-13-22, Miyakojimahondori, Miyakojima-ku, Osaka 534-0021, Japan.
| | - Katsuya Kobayashi
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Riki Matsumoto
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan; Division of Neurology, Kobe University Graduate School of Medicine, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan.
| | - Morito Inouchi
- Department of Respiratory Care and Sleep Control Medicine, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto, Japan.
| | - Masaya Togo
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Jumpei Togawa
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Kiyohide Usami
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Akihiro Shimotake
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Masao Matsuhashi
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Takayuki Kikuchi
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Kazumichi Yoshida
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Hisashi Kawawaki
- Department of Pediatric Neurology, Child and Adolescent Epilepsy Center, Osaka City General Hospital, 2-13-22, Miyakojimahondori, Miyakojima-ku, Osaka 534-0021, Japan.
| | - Nobukatsu Sawamoto
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan; Department of Human Health Sciences, Kyoto University Graduate School of Medicine, 53, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Takeharu Kunieda
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan; Department of Neurosurgery, Ehime University Graduate School of Medicine, Shitsukawa Toon City, Ehime 791-0295, Japan.
| | - Susumu Miyamoto
- Department of Neurosurgery, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Ryosuke Takahashi
- Department of Neurology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
| | - Akio Ikeda
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, 54, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan.
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23
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Charupanit K, Sen-Gupta I, Lin JJ, Lopour BA. Detection of anomalous high-frequency events in human intracranial EEG. Epilepsia Open 2020; 5:263-273. [PMID: 32524052 PMCID: PMC7278560 DOI: 10.1002/epi4.12397] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 04/09/2020] [Accepted: 04/09/2020] [Indexed: 11/23/2022] Open
Abstract
Objective High‐frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high‐frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non‐SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude‐based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125). Significance ADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non‐SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome.
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Affiliation(s)
- Krit Charupanit
- Biomedical Engineering University of California, Irvine Irvine CA USA
| | - Indranil Sen-Gupta
- Comprehensive Epilepsy Program Department of Neurology University of California, Irvine Irvine CA USA
| | - Jack J Lin
- Biomedical Engineering University of California, Irvine Irvine CA USA.,Comprehensive Epilepsy Program Department of Neurology University of California, Irvine Irvine CA USA
| | - Beth A Lopour
- Biomedical Engineering University of California, Irvine Irvine CA USA
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Abstract
In epilepsy research, the analysis of rodent electroencephalogram (EEG) has been performed by many laboratories with a variety of techniques. However, the acquisition and basic analysis of rodent EEG have only recently been standardized. Since a number of software platforms and increased computational power have become widely available, advanced rodent EEG analysis is now more accessible to investigators working with rodent models of epilepsy. In this review, the approach to the analysis of rodent EEG will be examined, including the evaluation of both epileptiform and background activity. Major caveats when employing these analyses, cellular and circuit-level correlates of EEG changes, and important differences between rodent and human EEG are also reviewed. The currently available techniques show great promise in gaining a deeper understanding of the complexities hidden within the EEG in rodent models of epilepsy.
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Affiliation(s)
- Atul Maheshwari
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA.,Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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25
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Migliorelli C, Bachiller A, Alonso JF, Romero S, Aparicio J, Jacobs-Le Van J, Mañanas MA, San Antonio-Arce V. SGM: a novel time-frequency algorithm based on unsupervised learning improves high-frequency oscillation detection in epilepsy. J Neural Eng 2020; 17:026032. [PMID: 32213672 DOI: 10.1088/1741-2552/ab8345] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
OBJECTIVE We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. APPROACH This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. MAIN RESULTS We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. SIGNIFICANCE The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.
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Affiliation(s)
- Carolina Migliorelli
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain. Department of Automatic Control (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain. Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Barcelona, Spain
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26
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Sciaraffa N, Klados MA, Borghini G, Di Flumeri G, Babiloni F, Aricò P. Double-Step Machine Learning Based Procedure for HFOs Detection and Classification. Brain Sci 2020; 10:E220. [PMID: 32276318 PMCID: PMC7226084 DOI: 10.3390/brainsci10040220] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 01/17/2023] Open
Abstract
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.
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Affiliation(s)
- Nicolina Sciaraffa
- Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy; (G.B.); (G.D.F.); (F.B.); (P.A.)
- BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
| | - Manousos A. Klados
- Department of Psychology, The University of Sheffield, International Faculty, City College, 54626 Thessaloniki, Greece;
| | - Gianluca Borghini
- Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy; (G.B.); (G.D.F.); (F.B.); (P.A.)
- BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
- IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina, 306, 00179 Rome, Italy
| | - Gianluca Di Flumeri
- Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy; (G.B.); (G.D.F.); (F.B.); (P.A.)
- BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
- IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina, 306, 00179 Rome, Italy
| | - Fabio Babiloni
- Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy; (G.B.); (G.D.F.); (F.B.); (P.A.)
- BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
- College of Computer Science and Technology, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou 310018, China
| | - Pietro Aricò
- Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy; (G.B.); (G.D.F.); (F.B.); (P.A.)
- BrainSigns srl, Lungotevere Michelangelo, 9, 00192 Rome, Italy
- IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Via Ardeatina, 306, 00179 Rome, Italy
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27
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Dirodi M, Tamilia E, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Localization of High-Frequency Oscillations in Children with Epilepsy: Validation against Intracranial Gold-Standard. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1555-1558. [PMID: 31946191 DOI: 10.1109/embc.2019.8857793] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Patients with medically refractory epilepsy (MRE) need surgical resection of the epileptogenic zone (EZ) to gain seizure-freedom. High-frequency oscillations (HFOs, > 80 Hz) are promising biomarkers of the EZ that are typically localized using intracranial electroencephalography (icEEG). The goal of this study was to localize the cortical generators of HFOs non-invasively using high-density (HD) EEG and magnetoencephalography (MEG) and validate the localization against the gold-standard given by the icEEGdefined HFO-zone. METHODS We analyzed simultaneous HDEEG and MEG data from six children with MRE who underwent icEEG and surgery. We detected interictal HFOs (80-160 Hz) on HD-EEG and MEG separately, using an inhouse automatic detector followed by visual human review, and distinguished between HFOs with and without spikes. We localized the cortical generators of each HFO on HD-EEG or MEG using the wavelet Maximum Entropy on the Mean (wMEM). For the HFOs localized in the brain area covered by icEEG, we estimated the localization error (Eloc) with respect to the gold-standard, and classified them as either concordant (Eloc≤15mm) or not. RESULTS We found that: (i) HD-EEG presented a higher rate of HFOs than MEG (1 vs 0.5 HFOs/min, p=0.031); (ii) HFOs without spikes were more likely to be localized outside the brain regions of interest (i.e. covered by icEEG) than HFOs with spikes; and (iii) both HD-EEG and MEG showed high precision to the gold-standard (92% and 96%). CONCLUSION We reported quantitative evidence that HDEEG and MEG can localize the HFO cortical generators with high precision to the icEEG gold-standard in children with MRE, suggesting that they may possibly limit the need for icEEG prior to surgery. We also showed that HFOs with spikes on HD-EEG/MEG are more likely to be epileptogenic than those independent from spikes, which may represent physiological events from normal brain.
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28
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Donos C, Mîndruţă I, Barborica A. Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision. Front Neurosci 2020; 14:183. [PMID: 32265622 PMCID: PMC7104802 DOI: 10.3389/fnins.2020.00183] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 02/19/2020] [Indexed: 12/15/2022] Open
Abstract
High-frequency oscillations >80 Hz (HFOs) have unique features distinguishing them from spikes and artifactual components that can be well-evidenced in the time-frequency representations. We introduce an unsupervised HFO detector that uses computer-vision algorithms to detect HFO landmarks on two-dimensional (2D) time-frequency maps. To validate the detector, we introduce an analytical model of the HFO based on a sinewave having a Gaussian envelope, for which analytical equations in time-frequency space can be derived, allowing us to establish a direct correspondence between common HFO detection criteria in the time domain with the ones in the frequency domain, used by the computer-vision detection algorithm. The detector identifies potential HFO events on the time-frequency representation, which are classified as true HFOs if criteria regarding the HFO's frequency, amplitude, and duration are met. The detector is validated on simulated HFOs according to the analytical model, in the presence of noise, with different signal-to-noise ratios (SNRs) ranging from −9 to 0 dB. The detector's sensitivity was 0.64 at an SNR of −9 dB, 0.98 at −6 dB, and >0.99 at −3 dB and 0 dB, while its positive prediction value was >0.95, regardless of the SNR. Using the same simulation dataset, our detector is benchmarked against four previously published HFO detectors. The F-measure, a combined metric that takes into account both sensitivity and positive prediction value, was used to compare detection algorithms. Our detector surpassed the other detectors at −6, −3, and 0 dB and had the second best F-score at −9 dB SNR after the MNI detector (0.77 vs. 0.83). The ability to detect HFOs in clinical recordings has been tested on a set of 36 intracranial electroencephalogram (EEG) channels in six patients, with 89% of the detections being validated by two independent reviewers. The results demonstrate that the unsupervised detection of HFOs based on their 2D features in time-frequency maps is feasible and has a performance comparable or better than the most used HFO detectors.
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Affiliation(s)
- Cristian Donos
- Physics Department, Bucharest University, Bucharest, Romania
| | - Ioana Mîndruţă
- Department of Neurology, Bucharest University Emergency Hospital, Bucharest, Romania.,Department of Neurology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
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Murai T, Hitomi T, Matsuhashi M, Matsumoto R, Kawamura Y, Kanda M, Takahashi R, Ikeda A. Scalp EEG Could Record Both Ictal Direct Current Shift and High-Frequency Oscillation Together Even With a Time Constant of 2 Seconds. J Clin Neurophysiol 2020; 37:191-194. [DOI: 10.1097/wnp.0000000000000670] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Ma K, Lai D, Chen Z, Zeng Z, Zhang X, Chen W, Zhang H. Automatic detection of High Frequency Oscillations (80-500Hz) based on Convolutional Neural Network in Human Intracerebral Electroencephalogram .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:5133-5136. [PMID: 31947014 DOI: 10.1109/embc.2019.8857774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recently, high-frequency oscillations (HFOs) of range 80-500 Hz in electroencephalogram (EEG) recordings of epilepsy patients are considered as a reliable marker of epileptic seizure. In the present work, an automatic detection of HFOs represents an isolated peak (an `island') in a time-frequency plot based on convolutional neural network (CNN) was proposed. Initially, three patients with medically intractable epilepsy were recruited. They underwent a presurgical monitoring individually with around 54-90 channels of intracranial electroencephalograph (iEEG). Then, a specific CNN with five layers was developed with a total of 18,400 time-frequency island pictures marked with a label of either a real HFO or a false HFO. They are in the range of 80-500 Hz in the recorded iEEGs of 312 hours. Besides, over 7940 pictures including 3970 real HFO events and 3970 false HFO events except the training set were used to evaluate the performance of the current proposed method. As a result, the obtained precision of HFO events, the value of the recall, and the F1 score of the proposed CNN were found to be 94.19%, 89.37%, and 91.71%, respectively. Additionally, the automatic detection time of each HFO event is limited within 1-3 seconds. In summary, the proposed HFOs detector with deep learning would be more efficient and useful in the diagnosis of epilepsy as compared with the current manual determination of each HFOs from a long-term multichannel iEEGs recordings.
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Lachner-Piza D, Jacobs J, Bruder JC, Schulze-Bonhage A, Stieglitz T, Dümpelmann M. Automatic detection of high-frequency-oscillations and their sub-groups co-occurring with interictal-epileptic-spikes. J Neural Eng 2020; 17:016030. [PMID: 31530748 DOI: 10.1088/1741-2552/ab4560] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE High-frequency-oscillations (HFO) and interictal-epileptic-spikes (IES) are spatial biomarkers of the epileptogenic-zone. Those HFO spatially and temporally co-occurring with IES (IES-HFO) are potentially superior biomarkers, their use is however challenged by the difficulty in detecting the low amplitude HFO riding the high-amplitude and steep-waveform of IES. We aim to develop an automatic HFO detector with an improved performance with respect to current methods and that also correctly distinguishes IES-HFO from IES occurring in isolation (isol-IES). We also aim to validate the biomarker-value of the automatic detections by utilizing them to localize a surrogate of the epileptogenic-zone. APPROACH We developed automatic-detectors of HFO-Ripples (80-250 Hz), HFO-FastRipples (250-500 Hz) and IES based on kernelized support-vector-machines (SVM). The training of the HFO-detectors was based on visually marked HFO and the parameter optimization during this training-process promoted the correct discernment between IES-HFO and isol-IES. Both HFO-detectors were benchmarked against other published detectors using databases with both visually marked and simulated gold-standards. The IES-detector was trained with a publicly available simulated database and tested against both simulated and visually marked gold-standards. To validate the detections' biomarker-value, the detectors were run on intracranial-EEGs from 8 patients and each with durations of 2-3 days, the detections' cumulated per-channel occurrence-rate was then used to predict the seizure-onset-zone as a surrogate of the epileptogenic-zone. MAIN RESULTS The HFO-detectors obtained at least 21 more F1-score points than previously published algorithms at the lowest signal-to-noise-ratio. The success achieved when discerning between IES-HFO and isol-IES was comparable to that of other published algorithms. The automatically detected IES-HFO and IES-Ripples showed the best biomarker-value to localize the epileptogenic-zone. SIGNIFICANCE The developed detectors are publicly available and provide a reliable tool to further study HFO, IES-HFO and their value as biomarkers. The putative higher biomarker value from IES-HFO was validated on automatically-detected, long-term data.
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Affiliation(s)
- Daniel Lachner-Piza
- Epilepsy Center, Medical Center-University of Freiburg, Breisacher Str. 64, 79106 Freiburg, Germany. BrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Georges-Kohler-Allee 79, Freiburg 79110, Germany
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A Long Short-Term Memory neural network for the detection of epileptiform spikes and high frequency oscillations. Sci Rep 2019; 9:19374. [PMID: 31852929 PMCID: PMC6920137 DOI: 10.1038/s41598-019-55861-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 12/02/2019] [Indexed: 12/02/2022] Open
Abstract
Over the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50–500 events (per class) from all patients from the 1st dataset. This ‘global’ network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.
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Motoi H, Jeong JW, Juhász C, Miyakoshi M, Nakai Y, Sugiura A, Luat AF, Sood S, Asano E. Quantitative analysis of intracranial electrocorticography signals using the concept of statistical parametric mapping. Sci Rep 2019; 9:17385. [PMID: 31758022 PMCID: PMC6874664 DOI: 10.1038/s41598-019-53749-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/04/2019] [Indexed: 11/23/2022] Open
Abstract
Statistical parametric mapping (SPM) is a technique with which one can delineate brain activity statistically deviated from the normative mean, and has been commonly employed in noninvasive neuroimaging and EEG studies. Using the concept of SPM, we developed a novel technique for quantification of the statistical deviation of an intracranial electrocorticography (ECoG) measure from the nonepileptic mean. We validated this technique using data previously collected from 123 patients with drug-resistant epilepsy who underwent resective epilepsy surgery. We determined how the measurement of statistical deviation of modulation index (MI) from the non-epileptic mean (rated by z-score) improved the performance of seizure outcome classification model solely based on conventional clinical, seizure onset zone (SOZ), and neuroimaging variables. Here, MI is a summary measure quantifying the strength of in-situ coupling between high-frequency activity at >150 Hz and slow wave at 3-4 Hz. We initially generated a normative MI atlas showing the mean and standard deviation of slow-wave sleep MI of neighboring non-epileptic channels of 47 patients, whose ECoG sampling involved all four lobes. We then calculated 'MI z-score' at each electrode site. SOZ had a greater 'MI z-score' compared to non-SOZ in the remaining 76 patients. Subsequent multivariate logistic regression analysis and receiver operating characteristic analysis to the combined data of all patients revealed that the full regression model incorporating all predictor variables, including SOZ and 'MI z-score', best classified the seizure outcome with sensitivity/specificity of 0.86/0.76. The model excluding 'MI z-score' worsened its sensitivity/specificity to 0.86/0.48. Furthermore, the leave-one-out analysis successfully cross-validated the full regression model. Measurement of statistical deviation of MI from the non-epileptic mean on invasive recording is technically feasible. Our analytical technique can be used to evaluate the utility of ECoG biomarkers in epilepsy presurgical evaluation.
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Affiliation(s)
- Hirotaka Motoi
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Pediatrics, Yokohama City University Medical Center, Yokohama, 2320024, Japan
| | - Jeong-Won Jeong
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Csaba Juhász
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurosurgery, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA, 92093, USA
| | - Yasuo Nakai
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Ayaka Sugiura
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Aimee F Luat
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Sandeep Sood
- Department of Neurosurgery, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA
| | - Eishi Asano
- Department of Pediatrics, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA.
- Department of Neurology, Children's Hospital of Michigan, Wayne State University, Detroit Medical Center, Detroit, MI, 48201, USA.
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Charupanit K, Nunez MD, Bernardo D, Bebin M, Krueger DA, Northrup H, Sahin M, Wu JY, Lopour BA. Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3116-3119. [PMID: 30441054 DOI: 10.1109/embc.2018.8513033] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG. We analyzed scalp EEG recordings of seven subjects and validated our detector and artifact rejection algorithm via visual review. Of the candidate events marked by the detector, 40% and 60% were confirmed to be ripples and fast ripples, respectively, by human visual review, making this algorithm suitable for supervised detection. Detected HFOs occurred at a rate of <1/min in most channels, and the average duration was 47 and 24 ms for ripples and fast ripples, respectively. The simplicity of the algorithm, with only a single parameter, enables the consistent application of automatic detection across recording modalities, and it could therefore be a tool for the assessment and localization of epileptic activity.
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Cao D, Chen Y, Liao J, Nariai H, Li L, Zhu Y, Zhao X, Hu Y, Wen F, Zhai Q. Scalp EEG high frequency oscillations as a biomarker of treatment response in epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS). Seizure 2019; 71:151-157. [PMID: 31351306 DOI: 10.1016/j.seizure.2019.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/28/2019] [Accepted: 05/29/2019] [Indexed: 02/08/2023] Open
Abstract
PURPOSE We investigated whether the presence of interictal scalp EEG high frequency oscillations (HFOs) in children with epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS) can predict seizure and cognitive outcome after steroid therapy. METHODS Twenty-two children with CSWS were prospectively enrolled and received methylprednisolone therapy. Interictal scalp HFOs, spike wave index (SWI) and intelligence quotient (IQ) were assessed before and after the treatment. The children were divided into two groups based on the early seizure reduction ratio at 2 weeks (≥50%, "response group"; otherwise "non-response group"). The "response group" was further divided into two subgroups ("relapse" and "non-relapse" subgroups) according to the late seizure outcome (after 3 months). RESULTS Interictal HFOs and electrical status epilepticus in sleep (ESES) (defined as SWI ≥ 85%) were detected in all children at the baseline. In the response with relapse group (n = 11), the detection ratio of HFOs was significantly higher than that of ESES at 2 weeks (81.2 vs. 27.3%), 3 months (90.9 vs. 36.4%), and 6 months (100 vs. 54.5%) post-therapy. In the non-response group (n = 4), both HFOs and ESES persisted in all children. The average IQ improved significantly only in the response with non-relapse group. The persistence of HFOs negatively correlated with both the average IQ, yet the persistence of ESES did not. CONCLUSION Interictal scalp HFOs may be a favorable non-invasive biomarker of predicting seizure and cognitive outcome in CSWS.
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Affiliation(s)
- Dezhi Cao
- Second Clinical Medical College, Southern Medical University, Guangzhou, Guangdong, China; Neurology Department, Shenzhen Children's Hospital, Guangdong, China; Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Yan Chen
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Jianxiang Liao
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Hiroki Nariai
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lin Li
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Yanwei Zhu
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Xia Zhao
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Yan Hu
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Feiqiu Wen
- Neurology Department, Shenzhen Children's Hospital, Guangdong, China
| | - Qiongxiang Zhai
- Second Clinical Medical College, Southern Medical University, Guangzhou, Guangdong, China; Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China.
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Zweiphenning WJEM, Keijzer HM, van Diessen E, van ‘t Klooster MA, van Klink NEC, Leijten FSS, van Rijen PC, van Putten MJAM, Braun KPJ, Zijlmans M. Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach. Epilepsia 2019; 60:1908-1920. [PMID: 31329277 PMCID: PMC6852371 DOI: 10.1111/epi.16296] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 01/11/2023]
Abstract
OBJECTIVE New insights into high-frequency electroencephalographic activity and network analysis provide potential tools to improve delineation of epileptic tissue and increase the chance of postoperative seizure freedom. Based on our observation of high-frequency oscillations "spreading outward" from the epileptic source, we hypothesize that measures of directed connectivity in the high-frequency range distinguish epileptic from healthy brain tissue. METHODS We retrospectively selected refractory epilepsy patients with a malformation of cortical development or tumor World Health Organization grade I/II who underwent epilepsy surgery with intraoperative electrocorticography for tailoring the resection based on spikes. We assessed directed functional connectivity in the theta (4-8 Hz), gamma (30-80 Hz), ripple (80-250 Hz), and fast ripple (FR; 250-500 Hz) bands using the short-time direct directed transfer function, and calculated the total, incoming, and outgoing propagation strength for each electrode. We compared network measures of electrodes covering the resected and nonresected areas separately for patients with good and poor outcome, and of electrodes with and without spikes, ripples, and FRs (group level: paired t test; patient level: Mann-Whitney U test). We selected the measure that could best identify the resected area and channels with epileptic events using the area under the receiver operating characteristic curve, and calculated the positive and negative predictive value, sensitivity, and specificity. RESULTS We found higher total and outstrength in the ripple and gamma bands in resected tissue in patients with good outcome (rippletotal : P = .01; rippleout : P = .04; gammatotal : P = .01; gammaout : P = .01). Channels with events showed lower total and instrength, and higher outstrength in the FR band, and higher total and outstrength in the ripple, gamma, and theta bands (FRtotal : P = .05; FRin : P < .01; FRout : P = .02; gammatotal : P < .01; gammain : P = .01; gammaout : P < .01; thetatotal : P = .01; thetaout : P = .01). The total strength in the gamma band was most distinctive at the channel level (positive predictive value [PPV]good = 74%, PPVpoor = 43%). SIGNIFICANCE Interictally, epileptic tissue is isolated in the FR band and acts as a driver up to the (fast) ripple frequency range. The gamma band total strength seems promising to delineate epileptic tissue intraoperatively.
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Affiliation(s)
- Willemiek J. E. M. Zweiphenning
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Hanneke M. Keijzer
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
- MIRA Institute for Biomedical Technology and Technical MedicineClinical Neurophysiology GroupUniversity of TwenteEnschedethe Netherlands
| | - Eric van Diessen
- Department of Pediatric NeurologyUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Maryse A. van ‘t Klooster
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Nicole E. C. van Klink
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Frans S. S. Leijten
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Peter C. van Rijen
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Michel J. A. M. van Putten
- MIRA Institute for Biomedical Technology and Technical MedicineClinical Neurophysiology GroupUniversity of TwenteEnschedethe Netherlands
| | - Kees P. J. Braun
- Department of Pediatric NeurologyUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
| | - Maeike Zijlmans
- Department of Neurology and NeurosurgeryUniversity Medical Center Utrecht Brain CenterUtrecht UniversityUtrechtthe Netherlands
- Epilepsy Foundation of the NetherlandsHeemstedethe Netherlands
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Park CJ, Hong SB. High Frequency Oscillations in Epilepsy: Detection Methods and Considerations in Clinical Application. J Epilepsy Res 2019; 9:1-13. [PMID: 31482052 PMCID: PMC6706641 DOI: 10.14581/jer.19001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 01/02/2019] [Accepted: 01/04/2019] [Indexed: 01/10/2023] Open
Abstract
High frequency oscillations (HFOs) is a brain activity observed in electroencephalography (EEG) in frequency ranges between 80–500 Hz. HFOs can be classified into ripples (80–200 Hz) and fast ripples (200–500 Hz) by their distinctive characteristics. Recent studies reported that both ripples and fast fipples can be regarded as a new biomarker of epileptogenesis and ictogenesis. Previous studies verified that HFOs are clinically important both in patients with mesial temporal lobe epilepsy and neocortical epilepsy. Also, in epilepsy surgery, patients with higher resection ratio of brain regions with HFOs showed better outcome than a group with lower resection ratio. For clinical application of HFOs, it is important to delineate HFOs accurately and discriminate them from artifacts. There have been technical improvements in detecting HFOs by developing various detection algorithms. Still, there is a difficult issue on discriminating clinically important HFOs among detected HFOs, where both quantitative and subjective approaches are suggested. This paper is a review on published HFO studies focused on clinical findings and detection techniques of HFOs as well as tips for clinical applications.
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Affiliation(s)
- Chae Jung Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Biomedical Research Institute (SBRI), Seoul, Korea
| | - Seung Bong Hong
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Samsung Biomedical Research Institute (SBRI), Seoul, Korea
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Interictal Slow and High-Frequency Oscillations: Is it an Epileptic Slow or Red Slow? J Clin Neurophysiol 2019; 36:166-170. [PMID: 30589767 DOI: 10.1097/wnp.0000000000000527] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE We reported the presence of interictal slow and high-frequency oscillations (HFOs) (IIS + HFO) and its temporal change so as to elucidate its clinical usefulness as a surrogate marker of epileptogenic zone in a patient with intractable focal epilepsy. METHODS We focused on one of the core electrodes of epileptogenicity, and investigated IIS + HFO in the pre- and post-segment of 30 minutes to all the 6 seizures. We adopted interictal slow in duration of 0.33 to 10 seconds, amplitude ≥50 μV and co-occurring with HFOs, and then divided into 5 groups depending on the amplitude of slow wave. RESULTS Before and after all the 6 seizures, the number of IIS + HFO was 2,890 at one electrode in the core epileptogenic zone. The number of IIS + HFO significantly decreased for 30 minutes after seizures. Furthermore, the number of IIS + HFO with the amplitude of 200 to 399 μV significantly decreased after seizures. CONCLUSIONS IIS + HFO with the amplitude of 200 to 399 μV was influenced by and decreased after seizures. It may reflect the core part of epileptogenic area as similarly as ictal direct current shifts and ictal HFOs do. IIS + HFO could be called as the term "red slow," which may be useful to delineate at least a part of the epileptogenic zone.
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Amiri M, Frauscher B, Gotman J. Interictal coupling of
HFO
s and slow oscillations predicts the seizure‐onset pattern in mesiotemporal lobe epilepsy. Epilepsia 2019; 60:1160-1170. [DOI: 10.1111/epi.15541] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 04/20/2019] [Accepted: 04/22/2019] [Indexed: 01/22/2023]
Affiliation(s)
- Mina Amiri
- Montreal Neurological Institute McGill University Montreal Quebec Canada
| | - Birgit Frauscher
- Montreal Neurological Institute McGill University Montreal Quebec Canada
| | - Jean Gotman
- Montreal Neurological Institute McGill University Montreal Quebec Canada
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Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing. APPL INTELL 2019. [DOI: 10.1007/s10489-019-01461-0] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Höller P, Trinka E, Höller Y. MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations. Front Neuroinform 2019; 13:20. [PMID: 31024284 PMCID: PMC6460903 DOI: 10.3389/fninf.2019.00020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 03/11/2019] [Indexed: 11/21/2022] Open
Abstract
High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.
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Affiliation(s)
- Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria,Department of Psychology, University of Akureyri, Akureyri, Iceland,*Correspondence: Yvonne Höller
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Santana-Gomez C, Andrade P, Hudson MR, Paananen T, Ciszek R, Smith G, Ali I, Rundle BK, Ndode-Ekane XE, Casillas-Espinosa PM, Immonen R, Puhakka N, Jones N, Brady RD, Perucca P, Shultz SR, Pitkänen A, O'Brien TJ, Staba R. Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis. Epilepsy Res 2019; 156:106110. [PMID: 30981541 DOI: 10.1016/j.eplepsyres.2019.03.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 03/12/2019] [Accepted: 03/14/2019] [Indexed: 01/25/2023]
Abstract
Studies of chronic epilepsy show pathological high frequency oscillations (HFOs) are associated with brain areas capable of generating epileptic seizures. Only a few of these studies have focused on HFOs during the development of epilepsy, but results suggest pathological HFOs could be a biomarker of epileptogenesis. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy" (EpiBioS4Rx) is a multi-center project designed to identify biomarkers of epileptogenesis after a traumatic brain injury (TBI) and evaluate treatments that could modify or prevent the development of post-traumatic epilepsy. One goal of the EpiBioS4Rx project is to assess whether HFOs could be a biomarker of post-traumatic epileptogenesis. The current study describes the work towards this goal, including the development of common surgical procedures and EEG protocols, an interim analysis of the EEG for HFOs, and identifying issues that need to be addressed for a robust biomarker analysis. At three participating sites - University of Eastern Finland (UEF), Monash University in Melbourne (Melbourne) and University of California, Los Angeles (UCLA) - TBI was induced in adult male Sprague-Dawley rats by lateral fluid-percussion injury. After injury and in sham-operated controls, rats were implanted with screw and microwire electrodes positioned in neocortex and hippocampus to record EEG. A separate group of rats had serial magnetic resonance imaging after injury and then implanted with electrodes at 6 months. Recordings 28 days post-injury were available from UEF and UCLA, but not Melbourne due to technical issues with their EEG files. Analysis of recordings from 4 rats - UEF and UCLA each had one TBI and one sham-operated control - showed EEG contained evidence of HFOs. Computer-automated algorithms detected a total of 1,819 putative HFOs and of these only 40 events (2%) were detected by all three sites. Manual review of all events verified 130 events as HFO and the remainder as false positives. Review of the 40 events detected by all three sites was associated with 88% agreement. This initial report from the EpiBioS4Rx Consortium demonstrates the standardization of EEG electrode placements, recording protocol and long-term EEG monitoring, and differences in detection algorithm HFO results between sites. Additional work on detection strategy, detection algorithm performance, and training in HFO review will be performed to establish a robust, preclinical evaluation of HFOs as a biomarker of post-traumatic epileptogenesis.
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Affiliation(s)
- Cesar Santana-Gomez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| | - Pedro Andrade
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Matthew R Hudson
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Tomi Paananen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Robert Ciszek
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Gregory Smith
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Idrish Ali
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Brian K Rundle
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | | | - Pablo M Casillas-Espinosa
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Riikka Immonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora Puhakka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Nigel Jones
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Rhys D Brady
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Piero Perucca
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Sandy R Shultz
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Terence J O'Brien
- The Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Australia; Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, VIC, 3052, Australia
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Thomschewski A, Hincapié AS, Frauscher B. Localization of the Epileptogenic Zone Using High Frequency Oscillations. Front Neurol 2019; 10:94. [PMID: 30804887 PMCID: PMC6378911 DOI: 10.3389/fneur.2019.00094] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 01/23/2019] [Indexed: 01/22/2023] Open
Abstract
For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.
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Affiliation(s)
- Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Center, Paracelsus Medical University, Salzburg, Austria
- Department of Psychology, Paris-Lodron University of Salzburg, Salzburg, Austria
| | - Ana-Sofía Hincapié
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Roehri N, Pizzo F, Lagarde S, Lambert I, Nica A, McGonigal A, Giusiano B, Bartolomei F, Bénar CG. High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes. Ann Neurol 2019; 83:84-97. [PMID: 29244226 DOI: 10.1002/ana.25124] [Citation(s) in RCA: 120] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 12/14/2017] [Accepted: 12/14/2017] [Indexed: 01/07/2023]
Abstract
OBJECTIVE High-frequency oscillations (HFOs) in intracerebral EEG (stereoelectroencephalography; SEEG) are considered as better biomarkers of epileptogenic tissues than spikes. How this can be applied at the patient level remains poorly understood. We investigated how well HFOs and spikes can predict epileptogenic regions with a large spatial sampling at the patient level. METHODS We analyzed non-REM sleep SEEG recordings sampled at 2,048Hz of 30 patients. Ripples (Rs; 80-250Hz), fast ripples (FRs; 250-500Hz), and spikes were automatically detected. Rates of these markers and several combinations-spikes co-occurring with HFOs or FRs and cross-rate (Spk⊗HFO)-were compared to a quantified measure of the seizure onset zone (SOZ) by performing a receiver operating characteristic analysis for each patient individually. We used a Wilcoxon signed-rank test corrected for false-discovery rate to assess whether a marker was better than the others for predicting the SOZ. RESULTS A total of 2,930 channels was analyzed (median of 100 channels per patient). The HFOs or any of its variants were not statistically better than spikes. Only one feature, the cross-rate, was better than all the other markers. Moreover, fast ripples, even though very specific, were not delineating all epileptogenic tissues. INTERPRETATION At the patient level, the performance of HFOs is weakened by the presence of strong physiological HFO generators. Fast ripples are not sensitive enough to be the unique biomarker of epileptogenicity. Nevertheless, combining HFOs and spikes using our proposed measure-the cross-rate-is a better strategy than using only one marker. Ann Neurol 2018;83:84-97.
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Affiliation(s)
- Nicolas Roehri
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Francesca Pizzo
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Stanislas Lagarde
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,APHM, Timone Hospital, Clinical Neurophysiology, Marseille, France
| | - Isabelle Lambert
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,APHM, Timone Hospital, Clinical Neurophysiology, Marseille, France
| | - Anca Nica
- CHU Rennes, Neurology, Rennes, France
| | - Aileen McGonigal
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,APHM, Timone Hospital, Clinical Neurophysiology, Marseille, France
| | - Bernard Giusiano
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,APHM, Public Health Department, Marseille, France
| | - Fabrice Bartolomei
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,APHM, Timone Hospital, Clinical Neurophysiology, Marseille, France
| | - Christian-George Bénar
- Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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Jiang C, Li X, Yan J, Yu T, Wang X, Ren Z, Li D, Liu C, Du W, Zhou X, Xing Y, Ren G, Zhang G, Yang X. Determining the Quantitative Threshold of High-Frequency Oscillation Distribution to Delineate the Epileptogenic Zone by Automated Detection. Front Neurol 2018; 9:889. [PMID: 30483204 PMCID: PMC6243027 DOI: 10.3389/fneur.2018.00889] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 10/01/2018] [Indexed: 01/29/2023] Open
Abstract
Objective: We proposed an improved automated high frequency oscillations (HFOs) detector that could not only be applied to various intracranial electrodes, but also automatically remove false HFOs caused by high-pass filtering. We proposed a continuous resection ratio of high order HFO channels and compared this ratio with each patient's post-surgical outcome, to determine the quantitative threshold of HFO distribution to delineate the epileptogenic zone (EZ). Methods: We enrolled a total of 43 patients diagnosed with refractory epilepsy. The patients were used to optimize the parameters for SEEG electrodes, to test the algorithm for identifying false HFOs, and to calculate the continuous resection ratio of high order HFO channels. The ratio can be used to determine a quantitative threshold to locate the epileptogenic zone. Results: Following optimization, the sensitivity, and specificity of our detector were 66.84 and 73.20% (ripples) and 69.76 and 66.13% (fast ripples, FRs), respectively. The sensitivity and specificity of our algorithm for removing false HFOs were 76.82 and 94.54% (ripples) and 72.55 and 94.87% (FRs), respectively. The median of the continuous resection ratio of high order HFO channels in patients with good surgical outcomes, was significantly higher than in patients with poor outcome, for both ripples and FRs (P < 0.05 ripples and P < 0.001 FRs). Conclusions: Our automated detector has the advantage of not only applying to various intracranial electrodes but also removing false HFOs. Based on the continuous resection ratio of high order HFO channels, we can set the quantitative threshold for locating epileptogenic zones.
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Affiliation(s)
- Chenxi Jiang
- Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaonan Li
- Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China
| | - Tao Yu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xueyuan Wang
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiwei Ren
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Donghong Li
- Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Wei Du
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaoxia Zhou
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yue Xing
- Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guojun Zhang
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaofeng Yang
- Center of Epilepsy, Center for Brain Disorders Research, Capital Medical University, Beijing, China.,Center of Epilepsy, Beijing Institute of Brain Disorders, Beijing, China.,Neuroelectrophysiological Laboratory, Xuanwu Hospital, Capital Medical University, Beijing, China
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Nariai H, Wu JY, Bernardo D, Fallah A, Sankar R, Hussain SA. Interrater reliability in visual identification of interictal high-frequency oscillations on electrocorticography and scalp EEG. Epilepsia Open 2018; 3:127-132. [PMID: 30564771 PMCID: PMC6293061 DOI: 10.1002/epi4.12266] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
High-frequency oscillations (HFOs), including ripples (Rs) and fast ripples (FRs), are promising biomarkers of epileptogenesis, but their clinical utility is limited by the lack of a standardized approach to identification. We set out to determine whether electroencephalographers experienced in HFO analysis can reliably identify and quantify interictal HFOs. Two blinded raters independently reviewed 10 intraoperative electrocorticography (ECoG) samples from epilepsy surgery cases, and 10 scalp EEG samples from epilepsy monitoring unit evaluations. HFOs were visually marked using bandpass filters (R, 80-250 Hz; FR, 250-500 Hz) with a sampling frequency of 2,000 Hz. There was agreement as to the presence or absence of epileptiform discharges (EDs), Rs, and FRs, in 17, 18, and 18 cases, respectively. Interrater reliability (IRR) was favorable with κ = 0.70, 0.80, and 0.80, respectively, and similar for ECoG and scalp electroencephalography (EEG). Furthermore, interclass correlation for rates of Rs (0.99, 95% confidence interval [CI] 0.96-0.99) and FRs (0.77, 95% CI 0.41-0.91) were superior in comparison to EDs (0.37, 95% CI -0.60 to 0.75). Our data suggest that HFO identification and quantification are reliable among experienced electroencephalographers. Our findings support the reliability of utilizing HFO data in both research and clinical arenas.
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Affiliation(s)
- Hiroki Nariai
- Division of Pediatric Neurology UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
| | - Joyce Y Wu
- Division of Pediatric Neurology UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
| | - Danilo Bernardo
- Division of Pediatric Neurology UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
| | - Aria Fallah
- Department of Neurosurgery UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
| | - Raman Sankar
- Division of Pediatric Neurology UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
| | - Shaun A Hussain
- Division of Pediatric Neurology UCLA Mattel Children's Hospital David Geffen School of Medicine Los Angeles California U.S.A
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Tamilia E, Park EH, Percivati S, Bolton J, Taffoni F, Peters JM, Grant PE, Pearl PL, Madsen JR, Papadelis C. Surgical resection of ripple onset predicts outcome in pediatric epilepsy. Ann Neurol 2018; 84:331-346. [PMID: 30022519 DOI: 10.1002/ana.25295] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 07/05/2018] [Accepted: 07/06/2018] [Indexed: 12/31/2022]
Abstract
OBJECTIVE In patients with medically refractory epilepsy (MRE), interictal ripples (80-250Hz) are observed in large brain areas whose resection may be unnecessary for seizure freedom. This limits their utility as epilepsy biomarkers for surgery. We assessed the spatiotemporal propagation of interictal ripples on intracranial electroencephalography (iEEG) in children with MRE, compared it with the propagation of spikes, identified ripples that initiated propagation (onset-ripples), and evaluated their clinical value as epilepsy biomarkers. METHODS Twenty-seven children who underwent epilepsy surgery were studied. We identified propagation sequences of ripples and spikes across multiple iEEG contacts and calculated each ripple or spike latency from the propagation onset. We classified ripples and spikes into categories (ie, onset, spread, and isolated) based on their spatiotemporal characteristics and correlated their mean rate inside and outside resection with outcome (good outcome, Engel 1 versus poor outcome, Engel≥2). We determined, as onset-zone, spread-zone, and isolated-zone, the areas generating the corresponding ripple or spike category and evaluated the predictive value of their resection. RESULTS We observed ripple propagation in all patients and spike propagation in 25 patients. Mean rate of onset-ripples inside resection predicted the outcome (odds ratio = 5.37; p = 0.02) and correlated with Engel class (rho = -0.55; p = 0.003). Resection of the onset-ripple-zone was associated with good outcome (p = 0.047). No association was found for the spread-ripple-zone, isolated-ripple-zone, or any spike-zone. INTERPRETATION Interictal ripples propagate across iEEG contacts in children with MRE. The association between the onset-ripple-zone resection and good outcome indicates that onset-ripples are promising epilepsy biomarkers, which estimate the epileptogenic tissue better than spread-ripples or onset-spikes. Ann Neurol 2018;84:331-346.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Eun-Hyoung Park
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Stefania Percivati
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Unit of Biomedical Robotics and Biomicrosystems, Engineering Department, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Fabrizio Taffoni
- Unit of Biomedical Robotics and Biomicrosystems, Engineering Department, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA
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Höller P, Trinka E, Höller Y. High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:1638097. [PMID: 30158959 PMCID: PMC6109569 DOI: 10.1155/2018/1638097] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 06/20/2018] [Accepted: 07/12/2018] [Indexed: 01/22/2023]
Abstract
High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.
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Affiliation(s)
- Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
| | - Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University, Salzburg, Austria
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Spring AM, Pittman DJ, Aghakhani Y, Jirsch J, Pillay N, Bello-Espinosa LE, Josephson C, Federico P. Generalizability of High Frequency Oscillation Evaluations in the Ripple Band. Front Neurol 2018; 9:510. [PMID: 30002645 PMCID: PMC6031752 DOI: 10.3389/fneur.2018.00510] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Accepted: 06/11/2018] [Indexed: 11/29/2022] Open
Abstract
Objective: We examined the interrater reliability and generalizability of high-frequency oscillation (HFO) visual evaluations in the ripple (80–250 Hz) band, and established a framework for the transition of HFO analysis to routine clinical care. We were interested in the interrater reliability or epoch generalizability to describe how similar the evaluations were between reviewers, and in the reviewer generalizability to represent the consistency of the internal threshold each individual reviewer. Methods: We studied 41 adult epilepsy patients (mean age: 35.6 years) who underwent intracranial electroencephalography. A morphology detector was designed and used to detect candidate HFO events, lower-threshold events, and distractor events. These events were subsequently presented to six expert reviewers, who visually evaluated events for the presence of HFOs. Generalizability theory was used to characterize the epoch generalizability (interrater reliability) and reviewer generalizability (internal threshold consistency) of visual evaluations, as well as to project the numbers of epochs, reviewers, and datasets required to achieve strong generalizability (threshold of 0.8). Results: The reviewer generalizability was almost perfect (0.983), indicating there were sufficient evaluations to determine the internal threshold of each reviewer. However, the interrater reliability for 6 reviewers (0.588) and pairwise interrater reliability (0.322) were both poor, indicating that the agreement of 6 reviewers is insufficient to reliably establish the presence or absence of individual HFOs. Strong interrater reliability (≥0.8) was projected as requiring a minimum of 17 reviewers, while strong reviewer generalizability could be achieved with <30 epoch evaluations per reviewer. Significance: This study reaffirms the poor reliability of using small numbers of reviewers to identify HFOs, and projects the number of reviewers required to overcome this limitation. It also provides a set of tools which may be used for training reviewers, tracking changes to interrater reliability, and for constructing a benchmark set of epochs that can serve as a generalizable gold standard, against which other HFO detection algorithms may be compared. This study represents an important step toward the reconciliation of important but discordant findings from HFO studies undertaken with different sets of HFOs, and ultimately toward transitioning HFO analysis into a meaningful part of the clinical epilepsy workup.
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Affiliation(s)
- Aaron M Spring
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Daniel J Pittman
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
| | - Yahya Aghakhani
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Jeffrey Jirsch
- Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Neelan Pillay
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Luis E Bello-Espinosa
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Department of Paediatrics, University of Calgary, Calgary, AB, Canada
| | - Colin Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Paolo Federico
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada.,Department of Radiology, University of Calgary, Calgary, AB, Canada
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Quitadamo LR, Mai R, Gozzo F, Pelliccia V, Cardinale F, Seri S. Kurtosis-Based Detection of Intracranial High-Frequency Oscillations for the Identification of the Seizure Onset Zone. Int J Neural Syst 2018; 28:1850001. [PMID: 29577781 DOI: 10.1142/s0129065718500016] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.
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Affiliation(s)
- Lucia Rita Quitadamo
- 1 School of Life and Health Sciences, Aston Brain Centre, Aston University, Birmingham, B4 7ET, UK
| | - Roberto Mai
- 2 Centro per la Chirurgia dell'Epilessia "Claudio Munari", Ospedale Ca' Granda-Niguarda, 20162 Milan, Italy
| | - Francesca Gozzo
- 2 Centro per la Chirurgia dell'Epilessia "Claudio Munari", Ospedale Ca' Granda-Niguarda, 20162 Milan, Italy
| | - Veronica Pelliccia
- 2 Centro per la Chirurgia dell'Epilessia "Claudio Munari", Ospedale Ca' Granda-Niguarda, 20162 Milan, Italy
| | - Francesco Cardinale
- 2 Centro per la Chirurgia dell'Epilessia "Claudio Munari", Ospedale Ca' Granda-Niguarda, 20162 Milan, Italy
| | - Stefano Seri
- 1 School of Life and Health Sciences, Aston Brain Centre, Aston University, Birmingham, B4 7ET, UK.,3 Department of Clinical Neurophysiology, The Birmingham Children's Hospital NHS, F. Trust, Birmingham, UK
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