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Schlafly ED, Carbonero D, Chu CJ, Kramer MA. A data augmentation procedure to improve detection of spike ripples in brain voltage recordings. Neurosci Res 2024:S0168-0102(24)00096-8. [PMID: 39102943 DOI: 10.1016/j.neures.2024.07.005] [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: 07/25/2024] [Revised: 06/04/2024] [Accepted: 07/30/2024] [Indexed: 08/07/2024]
Abstract
Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For patients with drug-resistant epilepsy, treatments include neurostimulation or surgical removal of the epileptogenic zone (EZ), the brain region responsible for seizure generation. Precise targeting of the EZ requires reliable biomarkers. Spike ripples - high-frequency oscillations that co-occur with large amplitude epileptic discharges - have gained prominence as a candidate biomarker. However, spike ripple detection remains a challenge. The gold-standard approach requires an expert manually visualize and interpret brain voltage recordings, which limits reproducibility and high-throughput analysis. Addressing these limitations requires more objective, efficient, and automated methods for spike ripple detection, including approaches that utilize deep neural networks. Despite advancements, dataset heterogeneity and scarcity severely limit machine learning performance. Our study explores long-short term memory (LSTM) neural network architectures for spike ripple detection, leveraging data augmentation to improve classifier performance. We highlight the potential of combining training on augmented and in vivo data for enhanced spike ripple detection and ultimately improving diagnostic accuracy in epilepsy treatment.
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Affiliation(s)
- Emily D Schlafly
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA.
| | - Daniel Carbonero
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA; Center for Systems Neuroscience, Boston University, Boston, MA, USA.
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Zhang Y, Liu L, Ding Y, Chen X, Monsoor T, Daida A, Oana S, Hussain S, Sankar R, Fallah A, Santana-Gomez C, Engel J, Staba RJ, Speier W, Zhang J, Nariai H, Roychowdhury V. PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. J Neural Eng 2024; 21:036023. [PMID: 38722308 PMCID: PMC11135143 DOI: 10.1088/1741-2552/ad4916] [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: 10/18/2023] [Revised: 04/19/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations.
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Affiliation(s)
- Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Lawrence Liu
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Yuanyi Ding
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Xin Chen
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Shaun Hussain
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Raman Sankar
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Cesar Santana-Gomez
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - Jerome Engel
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
- Department of Neurobiology, University of California, Los Angeles, CA, United States of America
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, United States of America
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA 90095, United States of America
| | - William Speier
- Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States of America
- Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States of America
| | - Jianguo Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China
| | - Hiroki Nariai
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, United States of America
| | - Vwani Roychowdhury
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, United States of America
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Wang Z, Guo J, van 't Klooster M, Hoogteijling S, Jacobs J, Zijlmans M. Prognostic Value of Complete Resection of the High-Frequency Oscillation Area in Intracranial EEG: A Systematic Review and Meta-Analysis. Neurology 2024; 102:e209216. [PMID: 38560817 PMCID: PMC11175645 DOI: 10.1212/wnl.0000000000209216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND AND OBJECTIVES High-frequency oscillations (HFOs; ripples 80-250 Hz; fast ripples [FRs] 250-500 Hz) recorded with intracranial electrodes generated excitement and debate about their potential to localize epileptogenic foci. We performed a systematic review and meta-analysis on the prognostic value of complete resection of the HFOs-area (crHFOs-area) for epilepsy surgical outcome in intracranial EEG (iEEG) accessing multiple subgroups. METHODS We searched PubMed, Embase, and Web of Science for original research from inception to October 27, 2022. We defined favorable surgical outcome (FSO) as Engel class I, International League Against Epilepsy class 1, or seizure-free status. The prognostic value of crHFOs-area for FSO was assessed by (1) the pooled FSO proportion after crHFOs-area; (2) FSO for crHFOs-area vs without crHFOs-area; and (3) the predictive performance. We defined high combined prognostic value as FSO proportion >80% + FSO crHFOs-area >without crHFOs-area + area under the curve (AUC) >0.75 and examined this for the clinical subgroups (study design, age, diagnostic type, HFOs-identification method, HFOs-rate thresholding, and iEEG state). Temporal lobe epilepsy (TLE) was compared with extra-TLE through dichotomous variable analysis. Individual patient analysis was performed for sex, affected hemisphere, MRI findings, surgery location, and pathology. RESULTS Of 1,387 studies screened, 31 studies (703 patients) met our eligibility criteria. Twenty-seven studies (602 patients) analyzed FRs and 20 studies (424 patients) ripples. Pooled FSO proportion after crHFOs-area was 81% (95% CI 76%-86%) for FRs and 82% (73%-89%) for ripples. Patients with crHFOs-area achieved more often FSO than those without crHFOs-area (FRs odds ratio [OR] 6.38, 4.03-10.09, p < 0.001; ripples 4.04, 2.32-7.04, p < 0.001). The pooled AUCs were 0.81 (0.77-0.84) for FRs and 0.76 (0.72-0.79) for ripples. Combined prognostic value was high in 10 subgroups: retrospective, children, long-term iEEG, threshold (FRs and ripples) and automated detection and interictal (FRs). FSO after complete resection of FRs-area (crFRs-area) was achieved less often in people with TLE than extra-TLE (OR 0.37, 0.15-0.89, p = 0.006). Individual patient analyses showed that crFRs-area was seen more in patients with FSO with than without MRI lesions (p = 0.02 after multiple correction). DISCUSSION Complete resection of the brain area with HFOs is associated with good postsurgical outcome. Its prognostic value holds, especially for FRs, for various subgroups. The use of HFOs for extra-TLE patients requires further evidence.
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Affiliation(s)
- Ziyi Wang
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Jiaojiao Guo
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maryse van 't Klooster
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Sem Hoogteijling
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Julia Jacobs
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
| | - Maeike Zijlmans
- From the Department of Neurology and Neurosurgery (Z.W., J.G., M.v.t.K., S.H., M.Z.), University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Part of ERN EpiCARE, the Netherlands; Department of Pediatrics (J.J.), University of Calgary, Alberta Children's Hospital, Calgary, Canada; and Stichting Epilepsie Instellingen Nederland (SEIN) (M.Z.), Heemstede, the Netherlands
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Qu Z, Luo J, Chen X, Zhang Y, Yu S, Shu H. Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis. J Neurol Surg A Cent Eur Neurosurg 2024; 85:294-301. [PMID: 37918885 PMCID: PMC10984718 DOI: 10.1055/a-2202-9344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND High-frequency oscillations (HFOs) are spontaneous electroencephalographic (EEG) events that occur within the frequency range of 80 to 500 Hz and consist of at least four distinct oscillations that stand out from the background activity. They can be further classified into "ripples" (80-250 Hz) and "fast ripples" (FR; 250-500 Hz) based on different frequency bands. Studies have indicated that HFOs may serve as important markers for identifying epileptogenic regions and networks in patients with refractory epilepsy. Furthermore, a higher extent of removal of brain regions generating HFOs could potentially lead to improved prognosis. However, the clinical application criteria for HFOs remain controversial, and the results from different research groups exhibit inconsistencies. Given this controversy, the aim of this study was to conduct a meta-analysis to explore the utility of HFOs in predicting postoperative seizure outcomes by examining the prognosis of refractory epilepsy patients with varying ratios of HFO removal. METHODS Prospective and retrospective studies that analyzed HFOs and postoperative seizure outcomes in epilepsy patients who underwent resective surgery were included in the meta-analysis. The patients in these studies were grouped based on the ratio of HFOs removed, resulting in four groups: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and partial ripples removal (P-Ripples). The prognosis of patients within each group was compared to investigate the correlation between the ratio of HFO removal and patient prognosis. RESULTS A total of nine studies were included in the meta-analysis. The prognosis of patients in the C-FR group was significantly better than that of patients with incomplete FR removal (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10-14.15; p < 0.00001). Similarly, patients in the C-Ripples group had a more favorable prognosis compared with those with incomplete ripples removal (OR = 4.45; 95% CI: 1.33-14.89; p = 0.02). Patients in the P-FR group had better prognosis than those with a majority of FR remaining untouched (OR = 6.23; 95% CI: 2.04-19.06; p = 0.001). In the P-Ripples group, the prognosis of patients with a majority of ripples removed was superior to that of patients with a majority of ripples remaining untouched (OR = 8.14; 95% CI: 2.62-25.33; p = 0.0003). CONCLUSIONS There is a positive correlation between the greater removal of brain regions generating HFOs and more favorable postoperative seizure outcomes. However, further investigations, particularly through clinical trials, are necessary to justify the clinical application of HFOs in guiding epilepsy surgery.
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Affiliation(s)
- Zhichuang Qu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Juan Luo
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Xin Chen
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Yuanyuan Zhang
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
| | - Sixun Yu
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
| | - Haifeng Shu
- Department of Neurosurgery, Affiliated Hospital of Southwest Medical University, Luzhou, China
- Department of Neurosurgery, The PLA Western Theater Command General Hospital, Chengdu, China
- Southwest Jiaotong University, Chengdu, China
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy. Clin Neurophysiol 2023; 154:129-140. [PMID: 37603979 PMCID: PMC10861270 DOI: 10.1016/j.clinph.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 06/30/2023] [Accepted: 07/26/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes.
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Affiliation(s)
- Tonmoy Monsoor
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Yipeng Zhang
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Shingo Oana
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Qiujing Lu
- 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
| | - Aria Fallah
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- 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
| | - Richard J Staba
- Department of Neurology, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - William Speier
- Department of Bioengineering, University of California, Los Angeles, CA, USA; Department of Radiological Sciences, 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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Li Z, Zhang H, Niu S, Xing Y. Localizing epileptogenic zones with high-frequency oscillations and directed connectivity. Seizure 2023; 111:9-16. [PMID: 37487273 DOI: 10.1016/j.seizure.2023.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI). METHODS The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve. RESULTS Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85. CONCLUSIONS cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of information transmission and signal processing, Yanshan University, Qinhuangdao 066004, China.
| | - Hao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Shipeng Niu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Yanyu Xing
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
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Edmonds B, Miyakoshi M, Gianmaria Remore L, Ahn S, Westley Phillips H, Daida A, Salamon N, Bari A, Sankar R, Matsumoto JH, Fallah A, Nariai H. Characteristics of ictal thalamic EEG in pediatric-onset neocortical focal epilepsy. Clin Neurophysiol 2023; 154:116-125. [PMID: 37595481 PMCID: PMC10529874 DOI: 10.1016/j.clinph.2023.07.007] [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: 03/13/2023] [Revised: 07/09/2023] [Accepted: 07/24/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVE To characterize ictal EEG change in the centromedian (CM) and anterior nucleus (AN) of the thalamus, using stereoelectroencephalography (SEEG) recordings. METHODS Forty habitual seizures were analyzed in nine patients with pediatric-onset neocortical drug-resistant epilepsy who underwent SEEG (age 2-25 y) with thalamic coverage. Both visual and quantitative analysis was used to evaluate ictal EEG signal in the cortex and thalamus. The amplitude and cortico-thalamic latencies of broadband frequencies at ictal onset were measured. RESULTS Visual analysis demonstrated consistent detection of ictal EEG changes in both the CM nucleus and AN nucleus with latency to thalamic ictal EEG changes of less than 400 ms in 95% of seizures, with low-voltage fast activity being the most common ictal pattern. Quantitative broadband amplitude analysis showed consistent power changes across the frequency bands, corresponding to ictal EEG onset, while while ictal EEG latency was variable from -18.0 seconds to 13.2 seconds. There was no significant difference between detection of CM and AN ictal activity on visual or amplitude analysis. Four patients with subsequent thalamic responsive neurostimulation (RNS) demonstrated ictal EEG changes consistent with SEEG findings. CONCLUSIONS Ictal EEG changes were consistently seen at the CM and AN of the thalamus during neocortical seizures. SIGNIFICANCE It may be feasible to use a closed-loop system in the thalamus to detect and modulate seizure activity for neocortical epilepsy.
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Affiliation(s)
- Benjamin Edmonds
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA.
| | - Makoto Miyakoshi
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, UCSD Medical Center, San Diego, CA, USA.
| | - Luigi Gianmaria Remore
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Samuel Ahn
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - H Westley Phillips
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Ausaf Bari
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- 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
| | - Joyce H Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, 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
| | - 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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8
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Edmonds B, Miyakoshi M, Remore LG, Ahn S, Phillips HW, Daida A, Salamon N, Bari A, Sankar R, Matsumoto JH, Fallah A, Nariai H. Characteristics of ictal thalamic EEG in pediatric-onset neocortical focal epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.22.23291714. [PMID: 37425697 PMCID: PMC10327240 DOI: 10.1101/2023.06.22.23291714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Objective To characterize ictal EEG change in the centromedian (CM) and anterior nucleus (AN) of the thalamus, using stereoelectroencephalography (SEEG) recordings. Methods Forty habitual seizures were analyzed in nine patients with pediatric-onset neocortical drug-resistant epilepsy who underwent SEEG (age 2-25 y) with thalamic coverage. Both visual and quantitative analysis was used to evaluate ictal EEG signal in the cortex and thalamus. The amplitude and cortico-thalamic latencies of broadband frequencies at ictal onset were measured. Results Visual analysis demonstrated consistent detection of ictal EEG changes in both the CM nucleus and AN nucleus with latency to thalamic ictal EEG changes of less than 400ms in 95% of seizures, with low-voltage fast activity being the most common ictal pattern. Quantitative broadband amplitude analysis showed consistent power changes across the frequency bands, corresponding to ictal EEG onset, while while ictal EEG latency was variable from -18.0 seconds to 13.2 seconds. There was no significant difference between detection of CM and AN ictal activity on visual or amplitude analysis. Four patients with subsequent thalamic responsive neurostimulation (RNS) demonstrated ictal EEG changes consistent with SEEG findings. Conclusions Ictal EEG changes were consistently seen at the CM and AN of the thalamus during neocortical seizures. Significance It may be feasible to use a closed-loop system in the thalamus to detect and modulate seizure activity for neocortical epilepsy.
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Affiliation(s)
- Benjamin Edmonds
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Makoto Miyakoshi
- Division of Child and Adolescent Psychiatry, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, UCSD Medical Center, San Diego, CA, USA
| | - Luigi Gianmaria Remore
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Samuel Ahn
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - H. Westley Phillips
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Atsuro Daida
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Ausaf Bari
- Department of Neurosurgery, UCLA Medical Center, David Geffen School of Medicine, Los Angeles, CA, USA
| | - Raman Sankar
- 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
| | - Joyce H. Matsumoto
- Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital, 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
| | - 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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9
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Maher C, Yang Y, Truong ND, Wang C, Nikpour A, Kavehei O. Seizure detection with reduced electroencephalogram channels: research trends and outlook. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230022. [PMID: 37153360 PMCID: PMC10154941 DOI: 10.1098/rsos.230022] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 04/11/2023] [Indexed: 05/09/2023]
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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Affiliation(s)
- Christina Maher
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Yikai Yang
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Nhan Duy Truong
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2050, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, New South Wales 2050, Australia
| | - Armin Nikpour
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales 2006, Australia
- Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales 2006, Australia
- Translational Research Collective, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Omid Kavehei
- School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
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10
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Monsoor T, Zhang Y, Daida A, Oana S, Lu Q, Hussain SA, Fallah A, Sankar R, Staba RJ, Speier W, Roychowdhury V, Nariai H. Optimizing Detection and Deep Learning-based Classification of Pathological High-Frequency Oscillations in Epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.13.23288435. [PMID: 37131743 PMCID: PMC10153337 DOI: 10.1101/2023.04.13.23288435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Objective This study aimed to explore sensitive detection methods and deep learning (DL)-based classification for pathological high-frequency oscillations (HFOs). Methods We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent resection after chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for pathological features based on spike association and time-frequency plot characteristics. A DL-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. Results The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors exhibited the most pathological features. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. Conclusions HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. Significance Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes. HIGHLIGHTS HFOs detected by the MNI detector showed different traits and higher pathological bias than those detected by the STE detectorHFOs detected by both MNI and STE detectors (the Intersection HFOs) were deemed the most pathologicalA deep learning-based classification was able to distill pathological HFOs, regard-less of the initial HFO detection methods.
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11
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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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12
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Gunnarsdottir KM, Li A, Smith RJ, Kang JY, Korzeniewska A, Crone NE, Rouse AG, Cheng JJ, Kinsman MJ, Landazuri P, Uysal U, Ulloa CM, Cameron N, Cajigas I, Jagid J, Kanner A, Elarjani T, Bicchi MM, Inati S, Zaghloul KA, Boerwinkle VL, Wyckoff S, Barot N, Gonzalez-Martinez J, Sarma SV. Source-sink connectivity: a novel interictal EEG marker for seizure localization. Brain 2022; 145:3901-3915. [PMID: 36412516 PMCID: PMC10200292 DOI: 10.1093/brain/awac300] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/05/2022] [Accepted: 08/01/2022] [Indexed: 07/26/2023] Open
Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.
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Affiliation(s)
| | - Adam Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Rachel J Smith
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Joon-Yi Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Adam G Rouse
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Jennifer J Cheng
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Michael J Kinsman
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Patrick Landazuri
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Utku Uysal
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Carol M Ulloa
- Department of Neurology, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Nathaniel Cameron
- Department of Neurosurgery, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Iahn Cajigas
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Jonathan Jagid
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Andres Kanner
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Turki Elarjani
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Manuel Melo Bicchi
- Department of Neurosurgery, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sara Inati
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Varina L Boerwinkle
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Sarah Wyckoff
- Barrow Neurological Institute, Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Niravkumar Barot
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | | | - Sridevi V Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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13
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Wang Y, Xu J, Liu T, Chen F, Chen S, Yuan L, Zhai F, Liang S. Diagnostic value of high-frequency oscillations for the epileptogenic zone: A systematic review and meta-analysis. Seizure 2022; 99:82-90. [DOI: 10.1016/j.seizure.2022.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/30/2022] Open
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14
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Dimakopoulos V, Gotman J, Stacey W, von Ellenrieder N, Jacobs J, Papadelis C, Cimbalnik J, Worrell G, Sperling MR, Zijlmans M, Imbach L, Frauscher B, Sarnthein J. Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom. Brain Commun 2022; 4:fcac151. [PMID: 35770134 PMCID: PMC9234061 DOI: 10.1093/braincomms/fcac151] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 04/29/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022] Open
Abstract
In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80-250 Hz) and in the fast ripple (250-500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient's HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Jean Gotman
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - William Stacey
- Department of Neurology and Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, MI, USA
| | | | - Julia Jacobs
- Alberta Children’s Hospital, University of Calgary, Calgary, Canada
| | | | - Jan Cimbalnik
- St. Anne’s University Hospital, Brno, Czech Republic
| | | | - Michael R Sperling
- Department of Neurology, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Maike Zijlmans
- University Medical Center, Utrecht, and Stichting Epilepsie Instellingen Nederland (SEIN), Utrecht, The Netherlands
| | - Lucas Imbach
- Schweizerisches Epilepsie Zentrum, Zurich, Switzerland
| | - Birgit Frauscher
- Montreal Neurological Institute & Hospital, McGill University, Montreal, Quebec, Canada
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
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15
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Burelo K, Sharifshazileh M, Indiveri G, Sarnthein J. Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks. Front Neurosci 2022; 16:861480. [PMID: 35720714 PMCID: PMC9205405 DOI: 10.3389/fnins.2022.861480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.
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Affiliation(s)
- Karla Burelo
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | | | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zurich, Switzerland
- Zentrum für Neurowissenschaften Zurich, ETH und Universität Zürich, Zurich, Switzerland
- *Correspondence: Johannes Sarnthein,
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16
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Billardello R, Ntolkeras G, Chericoni A, Madsen JR, Papadelis C, Pearl PL, Grant PE, Taffoni F, Tamilia E. Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery. Diagnostics (Basel) 2022; 12:diagnostics12041017. [PMID: 35454065 PMCID: PMC9032020 DOI: 10.3390/diagnostics12041017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 11/16/2022] Open
Abstract
Delineation of resected brain cavities on magnetic resonance images (MRIs) of epilepsy surgery patients is essential for neuroimaging/neurophysiology studies investigating biomarkers of the epileptogenic zone. The gold standard to delineate the resection on MRI remains manual slice-by-slice tracing by experts. Here, we proposed and validated a semiautomated MRI segmentation pipeline, generating an accurate model of the resection and its anatomical labeling, and developed a graphical user interface (GUI) for user-friendly usage. We retrieved pre- and postoperative MRIs from 35 patients who had focal epilepsy surgery, implemented a region-growing algorithm to delineate the resection on postoperative MRIs and tested its performance while varying different tuning parameters. Similarity between our output and hand-drawn gold standards was evaluated via dice similarity coefficient (DSC; range: 0-1). Additionally, the best segmentation pipeline was trained to provide an automated anatomical report of the resection (based on presurgical brain atlas). We found that the best-performing set of parameters presented DSC of 0.83 (0.72-0.85), high robustness to seed-selection variability and anatomical accuracy of 90% to the clinical postoperative MRI report. We presented a novel user-friendly open-source GUI that implements a semiautomated segmentation pipeline specifically optimized to generate resection models and their anatomical reports from epilepsy surgery patients, while minimizing user interaction.
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Affiliation(s)
- Roberto Billardello
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
- Correspondence: (R.B.); (E.T.)
| | - Georgios Ntolkeras
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Baystate Children’s Hospital, Springfield, MA 01199, USA
| | - Assia Chericoni
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Joseph R. Madsen
- Epilepsy Surgery Program, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Christos Papadelis
- Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX 76104, USA;
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Patricia Ellen Grant
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
| | - Fabrizio Taffoni
- Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Eleonora Tamilia
- Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; (G.N.); (A.C.); (P.E.G.)
- Correspondence: (R.B.); (E.T.)
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17
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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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18
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Dimakopoulos V, Mégevand P, Boran E, Momjian S, Seeck M, Vulliémoz S, Sarnthein J. Blinded study: prospectively defined high-frequency oscillations predict seizure outcome in individual patients. Brain Commun 2021; 3:fcab209. [PMID: 34541534 PMCID: PMC8445392 DOI: 10.1093/braincomms/fcab209] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 06/01/2021] [Accepted: 06/14/2020] [Indexed: 11/16/2022] Open
Abstract
Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery.
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Affiliation(s)
- Vasileios Dimakopoulos
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Pierre Mégevand
- Département des neurosciences fondamentales, Faculté de médecine, Université de Genève, Geneva, Switzerland.,Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Ece Boran
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland
| | - Shahan Momjian
- Service de neurochirurgie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Margitta Seeck
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Serge Vulliémoz
- Service de neurologie, Hôpitaux Universitaires de Genève, Geneva, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, UniversitätsSpital Zürich, Universität Zürich, Zürich, Switzerland.,Klinisches Neurowissenschaften Zentrum, University Hospital Zurich, Zürich, Switzerland
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19
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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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20
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Sharifshazileh M, Burelo K, Sarnthein J, Indiveri G. An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG. Nat Commun 2021; 12:3095. [PMID: 34035249 PMCID: PMC8149394 DOI: 10.1038/s41467-021-23342-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/20/2021] [Indexed: 02/04/2023] Open
Abstract
The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.
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Affiliation(s)
- Mohammadali Sharifshazileh
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Karla Burelo
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Johannes Sarnthein
- Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
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21
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Boran E, Stieglitz L, Sarnthein J. Epileptic High-Frequency Oscillations in Intracranial EEG Are Not Confounded by Cognitive Tasks. Front Hum Neurosci 2021; 15:613125. [PMID: 33746723 PMCID: PMC7971186 DOI: 10.3389/fnhum.2021.613125] [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: 10/01/2020] [Accepted: 01/27/2021] [Indexed: 11/13/2022] Open
Abstract
Rationale: High-frequency oscillations (HFOs) in intracranial EEG (iEEG) are used to delineate the epileptogenic zone during presurgical diagnostic assessment in patients with epilepsy. HFOs are historically divided into ripples (80-250 Hz), fast ripples (FR, >250 Hz), and their co-occurrence (FRandR). In a previous study, we had validated the rate of FRandRs during deep sleep to predict seizure outcome. Here, we ask whether epileptic FRandRs might be confounded by physiological FRandRs that are unrelated to epilepsy. Methods: We recorded iEEG in the medial temporal lobe MTL (hippocampus, entorhinal cortex, and amygdala) in 17 patients while they performed cognitive tasks. The three cognitive tasks addressed verbal working memory, visual working memory, and emotional processing. In our previous studies, these tasks activated the MTL. We re-analyzed the data of these studies with the automated detector that focuses on the co-occurrence of ripples and FRs (FRandR). Results: For each task, we identified those channels in which the HFO rate was modulated during the task condition compared to the control condition. However, the number of these channels did not exceed the chance level. Interestingly, even during wakefulness, the HFO rate was higher for channels within the seizure onset zone (SOZ) than for channels outside the SOZ. Conclusion: Our prospective definition of an epileptic HFO, the FRandR, is not confounded by physiological HFOs that might be elicited by our cognitive tasks. This is reassuring for the clinical use of FRandR as a biomarker of the EZ.
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Affiliation(s)
- Ece Boran
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
| | - Lennart Stieglitz
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
| | - Johannes Sarnthein
- Klinik für Neurochirurgie, Universitäts Spital und Universität Zürich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
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22
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Abstract
PURPOSE OF REVIEW Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures. RECENT FINDINGS We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making. SUMMARY New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.
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