Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy
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Abstract
Epilepsy diagnosis can be costly, time-consuming, and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-electroencephalography (EEG) monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially heralding a more quantitative approach to therapeutic outcomes. There is substantial research into the automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic, and despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG. This study reports on a deep learning algorithm for computer-assisted EEG review. Deep convolutional neural networks were trained to detect epileptic discharges using a preexisting dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data were curated and confirmed independently by two epilepsy specialists (Seneviratne et al., 2016). The resulting automated detection algorithm was then used to review diagnostic scalp EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting. The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes. This article is part of the Special Issue "NEWroscience 2018".
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Clarke et al. [170] achieved an average sensitivity of 95% and an average false positive rate of one per minute for detecting epileptic seizures from clinical EEGs.
“The system achieved an impressive average sensitivity of 95% and kept the average false positive rate to just one per minute.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [170]
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Assi, E. B., Schindler, K. A., de Bézenac, C. E., Denison, T., Desai, S. A., Keller, S. S., Lemoine, É., Rahimi, A., Shoaran, M., & Rummel, C. (2023) · Epilepsia · doi:10.1111/epi.17566 · Cited by 2 · see citing works
Higson, L., Finnin, N., Nurse, E. S., Cook, M. J., Winton‐Brown, T., Rayner, G., & Coleman, H. (2025) · Epilepsia · doi:10.1111/epi.18491 · Cited by 1 · see citing works
Self-citationReynolds, A., Lai, A., Grayden, D. B., Cook, M. J., & Peterson, A. (2025) · Epilepsy & Behavior Reports · doi:10.1016/j.ebr.2025.100829 · Cited by 1 · see citing works
Self-citationArnaout, B., Xu, A., Shadid, O. H., Karoly, P. J., Cook, M. J., & Nurse, E. S. (2025) · Epilepsia · doi:10.1002/epi.70072 · Cited by 1 · see citing works
Self-citationProgress in neural networks for EEG signal recognition in 2021Preprint
Rakhmatulin, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15755
Machine learning algorithms for diagnosis of epilepsy from EEG
Thangavel, P. (2022) · doi:10.32657/10356/156016
Deep learning for EEG analysis
Lourenço, C. D. S. (2023) · doi:10.3990/1.9789036556910
Fast Parametric Curve Matching (FPCM) Filters for Deep Learning-Based Automatic Spike Detection
Belokopytov, A. S., Kleeva, D. F., & Ossadtchi, A. E. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-80463-2_29
- Authors
- Clarke, S., Karoly, P. J., Nurse, E., Seneviratne, U., Taylor, J., Knight-Sadler, R., Kerr, R., Moore, B., Hennessy, P., Mendis, D., Lim, C., Miles, J., Cook, M., Freestone, D. R., & D'Souza, W.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Clarke, Shannon
- Venue
- Epilepsy & Behavior
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 121
- Issue
- Pt B
- Pages
- 106556
- ISSN
- 1525-5050 (ISSN-L)
- OpenAlex
- W2982348184
- Cited by
- 55
- References
- 46
- Citations by year
- 2025: 10 · 2024: 8 · 2023: 11 · 2022: 9 · 2021: 10 · 2020: 7
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Epilepsy & Behavior (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- idiopathic generalized epilepsy, deep convolutional neural networks, video electroencephalography, ambulatory EEG, interictal epileptiform discharges, seizure detection, diagnostic accuracy
- MeSH terms
- Algorithms, Computers, Electroencephalography, Epilepsy, Epilepsy, Generalized, Humans, Signal Processing, Computer-Assisted
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