Automated detection of interictal epileptiform discharges with few electroencephalographic channels

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis and management. New electroencephalographic (EEG) devices with fewer electrodes are more accessible, but their ability to detect IEDs is uncertain. The aim of this study is to determine whether IEDs can be reliably detected in reduced-channel EEG data, enabling broader epilepsy diagnosis. Using EEG samples from 3378 patients and an external validation set of 51 patients, we trained Cyclops, a deep neural network designed to function across various channel configurations. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and other clinically relevant metrics, including IED source location sensitivity. Cyclops demonstrated strong performance even with minimal channels. AUROC for one channel was .876 (95% confidence interval [CI] = .854-.897); best configuration based on a clinically available product was .950 (95% CI = .936-.962); for the detection of focal IEDs with two local channels, AUROC values ranged from .701 (95% CI = .656-.745) to .930 (95% CI = .902-.955), with a median AUROC of .809. On the external validation set, performance ranged from .692 (95% CI = .593-.782) to .949 (95% CI = .922-.972), with a median AUROC of .846. Thus, Cyclops demonstrates that effective IED detection is possible with reduced EEG setups, enhancing accessibility and expanding epilepsy diagnosis to broader patient populations.

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Publication Details

Journal
Epilepsia
Published
2025-05-03
DOI
https://doi.org/10.1111/epi.18431
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

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Automated detection of interictal epileptiform discharges with few electroencephalographic channels

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EEG and Brain-Computer Interfaces
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article

Automated detection of interictal epileptiform discharges with few electroencephalographic channels

Rani K. Singh, Elizabeth Waterhouse, Wolfgang Ganglberger, Jonathan J. Halford, Michael Brandon Westover, Joon‐Yi Kang, Robert J. Thomas, Maria Lopez, Jules C. Beal, Dennis Dlugos, Ezequiel L. Gleichgerrcht, Ji Yeoun Yoo, Chaoqi Yang, Michael Gelfand, Sarah E. Schmitt, Daniel M. Goldenholz, Brian C. Dean, Zeke Campbell, Kristin E. Wills, Liu Lin Thio, Ekrem Kutluay, Jing Jin, Mattia Galanti, Manu Hegde, Stephan J. Eisenschenk, Erik‐Jan Meulenbrugge, Moritz Alkofer
article en
1 citations

Abstract

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis and management. New electroencephalographic (EEG) devices with fewer electrodes are more accessible, but their ability to detect IEDs is uncertain. The aim of this study is to determine whether IEDs can be reliably detected in reduced-channel EEG data, enabling broader epilepsy diagnosis. Using EEG samples from 3378 patients and an external validation set of 51 patients, we trained Cyclops, a deep neural network designed to function across various channel configurations. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and other clinically relevant metrics, including IED source location sensitivity. Cyclops demonstrated strong performance even with minimal channels. AUROC for one channel was .876 (95% confidence interval [CI] = .854-.897); best configuration based on a clinically available product was .950 (95% CI = .936-.962); for the detection of focal IEDs with two local channels, AUROC values ranged from .701 (95% CI = .656-.745) to .930 (95% CI = .902-.955), with a median AUROC of .809. On the external validation set, performance ranged from .692 (95% CI = .593-.782) to .949 (95% CI = .922-.972), with a median AUROC of .846. Thus, Cyclops demonstrates that effective IED detection is possible with reduced EEG setups, enhancing accessibility and expanding epilepsy diagnosis to broader patient populations.

EpilepsiaVol. 66(7)
Beth Israel Deaconess Medical Center (US), Children's Hospital of Philadelphia (US), Harvard University (US), Johns Hopkins University (US), University of Miami (US), Medical University of South Carolina (US), University of Illinois Urbana-Champaign (US), University of California, San Francisco (US), Washington University in St. Louis (US), Cornell University (US), Emory University Hospital (US), Hunter Holmes McGuire VA Medical Center (US), University of Florida (US), Malcom Randall VA Medical Center (US), Bruce W. Carter VA Medical Center (US), Ralph H. Johnson VA Medical Center (US), University of Pennsylvania Health System (US), Technische Universität Berlin (DE), Clemson University (US), Icahn School of Medicine at Mount Sinai (US)
National Science Foundation, National Institutes of Health
Openalex Percentile: Top 20%
EEG and Brain-Computer Interfaces
1.43
DOIOpen Access PDFOpenAlex
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