Background and Objectives: The diagnostic yield of routine EEG in epilepsy is limited by low sensitivity and the potential for misinterpretation of interictal epileptiform discharges (IEDs). Our objective is to develop, train, and validate a deep learning model that can identify epilepsy from routine EEG recordings, complementing traditional IED-based interpretation. Methods: This is a retrospective cohort study of diagnostic accuracy. All consecutive patients undergoing routine EEG at our tertiary care center between January 2018 and September 2019 were included. EEGs recorded between July 2019 and September 2019 constituted a temporally shifted testing cohort. The diagnosis of epilepsy was established by the treating neurologist at the end of the available follow-up period, based on clinical file review. Original EEG reports were reviewed for IEDs. We developed seven novel deep learning models based on Vision Transformers (ViT) and Convolutional Neural Networks (CNN), training them to classify raw EEG recordings. We compared their performance to IED-based interpretation and two previously proposed machine learning methods. Results: The study included 948 EEGs from 846 patients (820 EEGs/728 patients in training/validation, 128 EEGs/118 patients in testing). Median follow-up was 2.2 years and 1.7 years in each cohort, respectively. Our flagship ViT model, DeepEpilepsy, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.69–0.83), outperforming IED-based interpretation (0.69; 0.64–0.73) and previous methods. Combining DeepEpilepsy with IEDs increased the AUROC to 0.83 (0.77–0.89). Discussion: DeepEpilepsy can identify epilepsy on routine EEG independently of IEDs, suggesting that deep learning can detect novel EEG patterns relevant to epilepsy diagnosis. Further research is needed to understand the exact nature of these patterns and evaluate the clinical impact of this increased diagnostic yield in specific settings.

The authors have declared no competing interest.

EL is supported by a scholarship from the Canadian Institutes of Health Research (CIHR) and the Fonds de Recherche du Quebec–Sante (FRQS). DKN and FL are supported by the Canada Research Chairs Program, the CIHR, and Natural Sciences and Engineering Research Council of Canada. DKN reports unrestricted educational grants from UCB and Eisai, and research grants for investigator-initiated studies from UCB and Eisai. EBA is supported by the Institute for Data Valorization (IVADO, 51628), the CHUM research center (51616), the Brain Canada Foundation (76097), and the FRQS. None of the authors declare any conflict of interest. The funding sources were not involved in study design, data collection, analysis, redaction, nor decision to submit this paper for publication.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics approval was granted by the CHUM Research Centre's Research Ethics Board (REB) (Montreal, Canada, project number: 19.334). The REB waived informed consent due to the lack of diagnostic/therapeutic intervention and minimal risk to participants. All methods followed Canada's Tri-Council Policy statement on Ethical Conduct for Research Involving Humans.

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The code for the study will be available upon publication at the following address: https://gitlab.com/chum-epilepsy/dl_epilepsy_reeg. Anonymized data will be made available to qualified investigators upon reasonable request, conditional to the approval by our REB. The STARD checklist is provided as Supplementary material.