Importance of methodological choices in data manipulation for validating epileptic seizure detection models
It addresses reproducibility and comparability issues in epilepsy detection for researchers and developers, but is incremental as it focuses on methodological guidance rather than new breakthroughs.
This paper tackles the problem of heterogeneous methodological approaches in epileptic seizure detection research by identifying and characterizing key methodological decisions, using an ensemble random-forest model on the CHB-MIT database to provide good-practice recommendations.
Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, difficulty comparing results, and low reproducibility. Therefore, this article identifies a wide range of methodological decisions that must be made and reported when training and evaluating the performance of epilepsy detection systems. We characterize the influence of individual choices using a typical ensemble random-forest model and the publicly available CHB-MIT database, providing a broader picture of each decision and giving good-practice recommendations, based on our experience, where possible.