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Epilepsy is a chronic neurological disorder that affects a significantportion of the human population and imposes serious risks in the daily life ofpatients. Despite advances in machine learning and IoT, small, nonstigmatizingwearable devices for continuous monitoring and detection in outpatientenvironments are not yet available. Part of the reason is the complexity ofepilepsy itself, including highly imbalanced data, multimodal nature, and verysubject-specific signatures. However, another problem is the heterogeneity ofmethodological approaches in research, leading to slower progress, difficultycomparing results, and low reproducibility. Therefore, this article identifiesa wide range of methodological decisions that must be made and reported whentraining and evaluating the performance of epilepsy detection systems. Wecharacterize the influence of individual choices using a typical ensemblerandom-forest model and the publicly available CHB-MIT database, providing abroader picture of each decision and giving good-practice recommendations,based on our experience, where possible.

Importance of methodological choices in data manipulation for validating epileptic seizure detection models