Application of an EEG-based deep learning model to discriminate children with epileptic spasms from normal controls

Objective Given that epileptic spasms are often subtle, and that identification of hypsarrhythmia is limited by inadequate inter-rater reliability, there is a significant need for novel tools to aid the clinical identification of Infantile Epileptic Spasms Syndrome (IESS). Deep learning is an emerging technology which may enable efficient classification of disease states and may facilitate discovery of novel biomarkers. In this study, we set out to evaluate whether children with epileptic spasms can be distinguished from normal controls with use of an EEG-based deep learning model. Methods A deep learning model was trained and validated (5-fold cross validation) using 400 EEG samples (2 awake and 2 sleep samples from 50 children with epileptic spasms and 50 normal controls). Salient frequency bands and specific morphologic EEG features were identified with occlusion sensitivity analysis and targeted input perturbation, respectively. Results The model accurately distinguishes children with epileptic spasms from normal controls, solely on the basis of relatively short EEG samples. Using sleep data, accuracy = 0.95, recall = 0.96, precision (sensitivity) = 0.94, specificity = 0.94, and F1 score = 0.95. With awake data, accuracy = 0.91, recall = 0.84, precision = 0.98, specificity = 0.98, and F1 score = 0.90. The salient frequency bands for classification are 9.7 – 22.0 Hz and 1.0 – 6.8 Hz in sleep and awake EEG, respectively. With visual analysis of extracted salient features, we suspect that the model is identifying cases on the basis of paroxysmal fast activity in sleep and spike-wave activity in wakefulness. Conclusion This deep learning model represents a first step in the development of efficient algorithms that may aid in identification of epileptic spasms and IESS. More importantly, this approach may facilitate novel EEG-based biomarkers of epileptic spasms. ### Competing Interest Statement Dr. Daida has received research support from the Uehara Memorial Foundation and the SENSHIN Medical Research Foundation. Dr. Rajaraman has received research support from Marinus Pharmaceuticals, GW Pharmaceuticals, the Pediatric Victory Foundation, and the International Foundation for CDKL5 Research (IFCR). Dr. Nariai is supported by the National Institute of Neurological Disorders and Stroke (NINDS) K23NS128318, the Sudha Neelakantan & Venky Harinarayan Charitable Fund, the Elsie and Isaac Fogelman Endowment, and the UCLA Children's Discovery and Innovation Institute (CDI) Junior Faculty Career Devel-opment Grant (#CDI-TTCF-07012021). Dr. Hussain has received research support from the John C. Hench Foundation, the CJDA Foundation, the Mohammed F. Alibrahim Endowment, the Elsie and Isaac Fogelman Endowment, the Epilepsy Therapy Project, the Milken Family Foundation, Paul Hughes Family Foundation, the Pediatric Epilepsy Research Foundation, Eisai, Bio-Pharm, Lundbeck, Insys, GW Pharmaceuticals, UCB Biopharma, Zogenix, Marinus, and the NIH. He has received compensation for service as a consultant to Amzell, Aquestive Therapeutics, Equilibre Biopharmaceuticals, Insys, GW Pharmaceuticals, Mallinckrodt, Marinus, MGC Pharmaceuticals, Radius, Shennox, UCB Biopharma, Upsher-Smith Laboratories, West Therapeutic Development, and Zogenix. ### Funding Statement This study was supported by the John C. Hench Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRB of the University of California, Los Angeles, gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors