Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.

Background: Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.
Methods: We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.
Results: We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.
Conclusion: The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.
Significance: We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
Competing Interests: ÉL is supported by a scholarship from the Canadian Institute of Health Research (CIHR). BR wishes to acknowledge financial support from the Centre for Clinical Brain Sciences of the University of Edinburgh, the CIHR, the Fonds de recherche du Québec—Santé (FRQS) and the Ministère de la Santé et des Services sociaux du Québec, and the Power Corporation of Canada Chair in Neurosciences of the University of Montreal. MRK and DKN report unrestricted educational grants from UCB and Eisai, and research grants for investigator-initiated studies from UCB and Eisai. DKN and FL are supported by the Canada Research Chairs Program, the Canadian Institutes of Health Research, and Natural Sciences and Engineering Research Council of Canada. OG is supported by the Institute for Data Valorization (IVADO). EBA is supported by IVADO (51628), the CHUM research center (51616), and the Brain Canada Foundation (76097). Funding sources had no role in the design or conduct of the study.
(© 2023 The Authors.)