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Nous A, Seynaeve L, Feys O, Wens V, De Tiège X, Van Mierlo P, Baroumand AG, Nieboer K, Allemeersch GJ, Mangelschots S, Michiels V, van der Zee J, Van Broeckhoven C, Ribbens A, Houbrechts R, De Witte S, Wittens MMJ, Bjerke M, Vanlersberghe C, Ceyssens S, Nagels G, Smolders I, Engelborghs S. Subclinical epileptiform activity in the Alzheimer continuum: association with disease, cognition and detection method. Alzheimers Res Ther 2024; 16:19. [PMID: 38263073 PMCID: PMC10804650 DOI: 10.1186/s13195-023-01373-9] [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: 09/13/2023] [Accepted: 12/17/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Epileptic seizures are an established comorbidity of Alzheimer's disease (AD). Subclinical epileptiform activity (SEA) as detected by 24-h electroencephalography (EEG) or magneto-encephalography (MEG) has been reported in temporal regions of clinically diagnosed AD patients. Although epileptic activity in AD probably arises in the mesial temporal lobe, electrical activity within this region might not propagate to EEG scalp electrodes and could remain undetected by standard EEG. However, SEA might lead to faster cognitive decline in AD. AIMS 1. To estimate the prevalence of SEA and interictal epileptic discharges (IEDs) in a well-defined cohort of participants belonging to the AD continuum, including preclinical AD subjects, as compared with cognitively healthy controls. 2. To evaluate whether long-term-EEG (LTM-EEG), high-density-EEG (hd-EEG) or MEG is superior to detect SEA in AD. 3. To characterise AD patients with SEA based on clinical, neuropsychological and neuroimaging parameters. METHODS Subjects (n = 49) belonging to the AD continuum were diagnosed according to the 2011 NIA-AA research criteria, with a high likelihood of underlying AD pathophysiology. Healthy volunteers (n = 24) scored normal on neuropsychological testing and were amyloid negative. None of the participants experienced a seizure before. Subjects underwent LTM-EEG and/or 50-min MEG and/or 50-min hd-EEG to detect IEDs. RESULTS We found an increased prevalence of SEA in AD subjects (31%) as compared to controls (8%) (p = 0.041; Fisher's exact test), with increasing prevalence over the disease course (50% in dementia, 27% in MCI and 25% in preclinical AD). Although MEG (25%) did not withhold a higher prevalence of SEA in AD as compared to LTM-EEG (19%) and hd-EEG (19%), MEG was significantly superior to detect spikes per 50 min (p = 0.002; Kruskall-Wallis test). AD patients with SEA scored worse on the RBANS visuospatial and attention subset (p = 0.009 and p = 0.05, respectively; Mann-Whitney U test) and had higher left frontal, (left) temporal and (left and right) entorhinal cortex volumes than those without. CONCLUSION We confirmed that SEA is increased in the AD continuum as compared to controls, with increasing prevalence with AD disease stage. In AD patients, SEA is associated with more severe visuospatial and attention deficits and with increased left frontal, (left) temporal and entorhinal cortex volumes. TRIAL REGISTRATION Clinicaltrials.gov, NCT04131491. 12/02/2020.
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Affiliation(s)
- Amber Nous
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Laboratory of Pharmaceutical Chemistry, Drug Analysis and Drug Information (FASC), Research Group Experimental Pharmacology (EFAR), Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Laura Seynaeve
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Odile Feys
- Department of Neurology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
| | - Vincent Wens
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Neuroimagerie Et Neuroanatomie Translationnelles (LN2T), Université Libre de Bruxelles (ULB), ULB Neuroscience Institute (UNI), Brussels, Belgium
- Department of Translational Neuroimaging, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), Hôpital Erasme, Brussels, Belgium
| | | | | | - Koenraad Nieboer
- Department of Radiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Department of Radiology, Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel, Brussels, Belgium
| | - Shana Mangelschots
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
| | - Veronique Michiels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Julie van der Zee
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Neurodegenerative Brain Diseases, VIB Center for Molecular Neurology, Antwerp, Belgium
| | | | | | - Sara De Witte
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
| | - Mandy Melissa Jane Wittens
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
| | - Maria Bjerke
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium
- Department of Clinical Biology, Laboratory of Clinical Neurochemistry, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Caroline Vanlersberghe
- Department of Anaesthesiology and Perioperative Medicine, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Universitair Ziekenhuis Antwerpen, University of Antwerp, Antwerpen, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Artificial Intelligence Supported Modelling in Clinical Sciences (AIMS) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Ilse Smolders
- Laboratory of Pharmaceutical Chemistry, Drug Analysis and Drug Information (FASC), Research Group Experimental Pharmacology (EFAR), Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences, Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels, Belgium.
- Department of Biomedical Sciences, Universiteit Antwerpen, Antwerp, Belgium.
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Pan R, Yang C, Li Z, Ren J, Duan Y. Magnetoencephalography-based approaches to epilepsy classification. Front Neurosci 2023; 17:1183391. [PMID: 37502686 PMCID: PMC10368885 DOI: 10.3389/fnins.2023.1183391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.
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Affiliation(s)
- Ruoyao Pan
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
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Hirano R, Emura T, Nakata O, Nakashima T, Asai M, Kagitani-Shimono K, Kishima H, Hirata M. Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2879-2890. [PMID: 35536808 DOI: 10.1109/tmi.2022.3173743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interictal spikes. Neurophysiologists visually identify spikes from the MEG waveforms and estimate the equivalent current dipoles (ECD). However, presently, these analyses are manually performed by neurophysiologists and are time-consuming. Another problem is that spike identification from MEG waveforms largely depends on neurophysiologists' skills and experiences. These problems cause poor cost-effectiveness in clinical MEG examination. To overcome these problems, we fully automated spike identification and ECD estimation using a deep learning approach fully automated AI-based MEG interictal epileptiform discharge identification and ECD estimation (FAMED). We applied a semantic segmentation method, which is an image processing technique, to identify the appropriate times between spike onset and peak and to select appropriate sensors for ECD estimation. FAMED was trained and evaluated using clinical MEG data acquired from 375 patients. FAMED training was performed in two stages: in the first stage, a classification network was learned, and in the second stage, a segmentation network that extended the classification network was learned. The classification network had a mean AUC of 0.9868 (10-fold patient-wise cross-validation); the sensitivity and specificity were 0.7952 and 0.9971, respectively. The median distance between the ECDs estimated by the neurophysiologists and those using FAMED was 0.63 cm. Thus, the performance of FAMED is comparable to that of neurophysiologists, and it can contribute to the efficiency and consistency of MEG ECD analysis.
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Yu Y, Chen Y, Li Y, Gao Z, Gai Z, Zhou Y. SQNN: A Spike-wave index Quantification Neural Network with a pre-labeling algorithm for epileptiform activity identification and quantification in children. J Neural Eng 2022; 19. [PMID: 35147524 DOI: 10.1088/1741-2552/ac542e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 02/09/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Electrical status epilepticus during slow sleep (ESES) is a phenomenon identified by strong activation of epileptiform activity in the electroencephalogram (EEG) during sleep. For children disturbed by ESES, spike-wave index (SWI) is defined to quantify the epileptiform activity in the EEG during sleep. Accurate SWI quantification is important for clinical diagnosis and prognosis. To quantify SWI automatically, a deep learning method is proposed in this paper. APPROACH Firstly, a pre-labeling algorithm (PreLA) composed of the adaptive wavelet enhanced decomposition and a slow-wave discrimination rule is designed to efficiently label the EEG signal. It enables the collection of large-scale EEG dataset with fine-grained labels. Then, an SWI Quantification Neural Network (SQNN) is constructed to accurately classify each sample point as normal or abnormal and to identify the abnormal events. SWI can be calculated automatically based on the total duration of abnormalities and the length of the signal. MAIN RESULTS Experiments on two datasets demonstrate that the PreLA is effective and robust for labeling the EEG data and the SQNN accurately and reliably quantifies SWI without using any thresholds. The average estimation error of SWI is 3.12%, indicating that our method is more accurate and robust than experts and previous related works. The processing speed of SQNN is 100 times faster than that of experts. SIGNIFICANCE Deep learning provides a novel approach to automatic SWI quantification and PreLA provides an easy way to label the EEG data with ESES syndromes. The results of the experiments indicate that the proposed method has a high potential for clinical diagnosis and prognosis of epilepsy in children.
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Affiliation(s)
- Yifei Yu
- Shanghai Jiao Tong University - Minhang Campus, 800 Dongchuan RD. Minhang District, Shanghai, 200240, CHINA
| | - Yehong Chen
- Qilu Children's Hospital of Shandong University, No. 23976, Jingshi Road, Huaiyin District, Jinan, 250022, CHINA
| | - Yuanxiang Li
- Shanghai Jiao Tong University - Minhang Campus, 800 Dongchuan RD. Minhang District, Shanghai, 200240, CHINA
| | - Zaifen Gao
- Qilu Children's Hospital of Shandong University, No. 23976, Jingshi Road, Huaiyin District, Jinan, 250022, CHINA
| | - Zhongtao Gai
- Qilu Children's Hospital of Shandong University, No. 23976, Jingshi Road, Huaiyin District, Jinan, 250022, CHINA
| | - Yunqing Zhou
- Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine, No.1678 Dongfang Road, Pudong New District, Shanghai, 200127, CHINA
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Cuesta P, Ochoa-Urrea M, Funke M, Hasan O, Zhu P, Marcos A, López ME, Schulz PE, Lhatoo S, Pantazis D, Mosher JC, Maestu F. OUP accepted manuscript. Brain Commun 2022; 4:fcac012. [PMID: 35282163 PMCID: PMC8914494 DOI: 10.1093/braincomms/fcac012] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 11/29/2021] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Pablo Cuesta
- Department of Radiology, Rehabilitation and Physiotherapy, Complutense University of Madrid, Madrid, Spain
- Correspondence to: Pablo Cuesta Prieto, Associate professor Department of Radiology, Rehabilitation and Physiotherapy, Medicine School Complutense University of Madrid Plaza, Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Manuela Ochoa-Urrea
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Omar Hasan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ping Zhu
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alberto Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| | - Maria Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Samden Lhatoo
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA
| | - John C. Mosher
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fernando Maestu
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
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Laohathai C, Ebersole JS, Mosher JC, Bagić AI, Sumida A, Von Allmen G, Funke ME. Practical Fundamentals of Clinical MEG Interpretation in Epilepsy. Front Neurol 2021; 12:722986. [PMID: 34721261 PMCID: PMC8551575 DOI: 10.3389/fneur.2021.722986] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/06/2021] [Indexed: 11/29/2022] Open
Abstract
Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.
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Affiliation(s)
- Christopher Laohathai
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
- Department of Neurology, Saint Louis University, Saint Louis, MO, United States
| | - John S. Ebersole
- Northeast Regional Epilepsy Group, Atlantic Health Neuroscience Institute, Summit, NJ, United States
| | - John C. Mosher
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Anto I. Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center, Pittsburg, PA, United States
| | - Ai Sumida
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Gretchen Von Allmen
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Michael E. Funke
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
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Zheng L, Liao P, Luo S, Sheng J, Teng P, Luan G, Gao JH. EMS-Net: A Deep Learning Method for Autodetecting Epileptic Magnetoencephalography Spikes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1833-1844. [PMID: 31831410 DOI: 10.1109/tmi.2019.2958699] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Epilepsy is a neurological disorder characterized by sudden and unpredictable epileptic seizures, which incurs significant negative impacts on patients' physical, psychological and social health. A practical approach to assist with the clinical assessment and treatment planning for patients is to process magnetoencephalography (MEG) data to identify epileptogenic zones. As a widely accepted biomarker of epileptic foci, epileptic MEG spikes need to be precisely detected. Given that the visual inspection of spikes is time consuming, an automatic and efficient system with adequate accuracy for spike detection is valuable in clinical practice. However, current approaches for MEG spike autodetection are dependent on hand-engineered features. Here, we propose a novel multiview Epileptic MEG Spikes detection algorithm based on a deep learning Network (EMS-Net) to accurately and efficiently recognize the spike events from MEG raw data. The results of the leave-k-subject-out validation tests for multiple datasets (i.e., balanced and realistic datasets) showed that EMS-Net achieved state-of-the-art classification performance (i.e., accuracy: 91.82% - 99.89%; precision: 91.90% - 99.45%; sensitivity: 91.61% - 99.53%; specificity: 91.60% - 99.96%; f1 score: 91.70% - 99.48%; and area under the curve: 0.9688 - 0.9998).
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Migliorelli C, Alonso JF, Romero S, Nowak R, Russi A, Mañanas MA. Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors. J Neural Eng 2018; 14:046013. [PMID: 28327467 DOI: 10.1088/1741-2552/aa684c] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. APPROACH Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. MAIN RESULTS ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. SIGNIFICANCE The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.
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Affiliation(s)
- Carolina Migliorelli
- Department of Automatic Control (ESAII), Biomedical Engineering Research Center (CREB), Universitat Politènica de Catalunya (UPC), Barcelona, Spain. Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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Abd El-Samie FE, Alotaiby TN, Khalid MI, Alshebeili SA, Aldosari SA. A Review of EEG and MEG Epileptic Spike Detection Algorithms. IEEE ACCESS 2018; 6:60673-60688. [DOI: 10.1109/access.2018.2875487] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN. JOURNAL OF HEALTHCARE ENGINEERING 2017; 2017:3035606. [PMID: 29118962 PMCID: PMC5651155 DOI: 10.1155/2017/3035606] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Revised: 08/06/2017] [Accepted: 09/13/2017] [Indexed: 11/18/2022]
Abstract
Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG) is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.
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Deviations from Critical Dynamics in Interictal Epileptiform Activity. J Neurosci 2017; 36:12276-12292. [PMID: 27903734 DOI: 10.1523/jneurosci.0809-16.2016] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 10/09/2016] [Accepted: 10/11/2016] [Indexed: 11/21/2022] Open
Abstract
The framework of criticality provides a unifying perspective on neuronal dynamics from in vitro cortical cultures to functioning human brains. Recent findings suggest that a healthy cortex displays critical dynamics, giving rise to scale-free spatiotemporal cascades of activity, termed neuronal avalanches. Pharmacological manipulations of the excitation-inhibition balance (EIB) in cortical cultures were previously shown to result in deviations from criticality and from the power law scaling of avalanche size distribution. To examine the sensitivity of neuronal avalanche metrics to altered EIB in humans, we focused on epilepsy, a neurological disorder characterized by hyperexcitable networks. Using magnetoencephalography, we quantitatively assessed deviations from criticality in the brain dynamics of patients with epilepsy during interictal (between-seizures) activity. Compared with healthy control subjects, epilepsy patients tended to exhibit a higher neural gain and larger avalanches, particularly during interictal epileptiform activity. Moreover, deviations from scale-free behavior were exclusively connected to brief intervals at epileptiform discharges, strengthening the association between deviations from criticality and the instantaneous changes in EIB. The avalanches collected during interictal epileptiform activity had not only a stereotypical size range but also involved particular spatial patterns of activations, as expected for periods of epileptic network dominance. Overall, the neuronal avalanche metrics provide a quantitative novel description of interictal brain activity of patients with epilepsy. SIGNIFICANCE STATEMENT Healthy brain dynamics requires a delicate balance between excitatory and inhibitory processes. Several brain disorders, such as epilepsy, are associated with altered excitation-inhibition balance, but assessing this balance using noninvasive tools is still challenging. In this study, we apply the framework of critical brain dynamics to data from epilepsy patients, which were recorded between seizures. We show that metrics of criticality provide a sensitive tool for noninvasive assessment of changes in the balance. Specifically, brain activity of epilepsy patients deviates from healthy critical brain dynamics, particularly during abnormal epileptiform activity. The study offers a novel quantitative perspective on epilepsy and its relation to healthy brain dynamics.
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Migliorelli C, Alonso JF, Romero S, Mañanas MA, Nowak R, Russi A. Influence of metallic artifact filtering on MEG signals for source localization during interictal epileptiform activity. J Neural Eng 2016; 13:026029. [DOI: 10.1088/1741-2560/13/2/026029] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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13
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Vvedensky VL, Prokofyev AO. Timing of Cortical Events Preceding Voluntary Movement. Neural Comput 2015; 28:286-304. [PMID: 26654207 DOI: 10.1162/neco_a_00802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
We studied magnetic signals from the human brain recorded during a second before a self-paced finger movement. Sharp triangular peaks were observed in the averaged signals about 0.7 second before the finger movement. The amplitude of the peaks varied considerably from trial to trial, which indicated that the peaks were concurrent with much longer oscillatory processes. One can cluster trials into distinct groups with characteristic sequences of events. Prominent short trains of pulses in the beta frequency band were identified in the premovement period. This observation suggests that during preparation of the intended movement, cortical activity is well organized in time but differs from trial to trial. Magnetoencephalography can capture these processes with high temporal resolution and allows their study in fine detail.
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Affiliation(s)
- Victor L Vvedensky
- MEG Center, Moscow State University of Psychology and Education, Moscow, Russian Federation, 107045, and Kurchatov Institute, Moscow, Russian Federation, 123182
| | - Andrey O Prokofyev
- MEG Center, Moscow State University of Psychology and Education, Kurchatov University, Moscow, Russian Federation, 107045
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Kim H, Chung CK, Hwang H. Magnetoencephalography in pediatric epilepsy. KOREAN JOURNAL OF PEDIATRICS 2013; 56:431-8. [PMID: 24244211 PMCID: PMC3827491 DOI: 10.3345/kjp.2013.56.10.431] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Accepted: 06/04/2013] [Indexed: 11/27/2022]
Abstract
Magnetoencephalography (MEG) records the magnetic field generated by electrical activity of cortical neurons. The signal is not distorted or attenuated, and it is contactless recording that can be performed comfortably even for longer than an hour. It has excellent and decent temporal resolution, especially when it is combined with the patient's own brain magnetic resonance imaging (magnetic source imaging). Data of MEG and electroencephalography are not mutually exclusive and it is recorded simultaneously and interpreted together. MEG has been shown to be useful in detecting the irritative zone in both lesional and nonlesional epilepsy surgery. It has provided valuable and additive information regarding the lesion that should be resected in epilepsy surgery. Better outcomes in epilepsy surgery were related to the localization of the irritative zone with MEG. The value of MEG in epilepsy surgery is recruiting more patients to epilepsy surgery and providing critical information for surgical planning. MEG cortical mapping is helpful in younger pediatric patients, especially when the epileptogenic zone is close to the eloquent cortex. MEG is also used in both basic and clinical research of epilepsy other than surgery. MEG is a valuable diagnostic modality for diagnosis and treatment, as well as research in epilepsy.
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Affiliation(s)
- Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea
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Heers M, Rampp S, Stefan H, Urbach H, Elger CE, von Lehe M, Wellmer J. MEG-based identification of the epileptogenic zone in occult peri-insular epilepsy. Seizure 2012; 21:128-33. [PMID: 22118838 DOI: 10.1016/j.seizure.2011.10.005] [Citation(s) in RCA: 51] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2011] [Revised: 10/22/2011] [Accepted: 10/24/2011] [Indexed: 11/19/2022] Open
Affiliation(s)
- Marcel Heers
- Ruhr-Epileptology, University Hospital Knappschaftskrankenhaus Bochum, Germany.
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Modeling of the Neurovascular Coupling in Epileptic Discharges. Brain Topogr 2011; 25:136-56. [DOI: 10.1007/s10548-011-0190-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2011] [Accepted: 06/07/2011] [Indexed: 10/18/2022]
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17
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Heers M, Rampp S, Kaltenhäuser M, Kasper BS, Doelken MT, Stefan H. Monofocal MEG in lesional TLE: Does video EEG monitoring add crucial information? Epilepsy Res 2010; 92:54-62. [DOI: 10.1016/j.eplepsyres.2010.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2010] [Revised: 08/08/2010] [Accepted: 08/15/2010] [Indexed: 11/27/2022]
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Heers M, Rampp S, Kaltenhäuser M, Pauli E, Rauch C, Dölken M, Stefan H. Detection of epileptic spikes by magnetoencephalography and electroencephalography after sleep deprivation. Seizure 2010; 19:397-403. [DOI: 10.1016/j.seizure.2010.06.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2010] [Revised: 04/27/2010] [Accepted: 06/04/2010] [Indexed: 10/19/2022] Open
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