On the performance of seizure prediction machine learning methods across different databases: the sample and alarm-based perspectives.

Epilepsy affects 1% of the global population, with approximately one-third of patients resistant to anti-seizure medications (ASMs), posing risks of physical injuries and psychological issues. Seizure prediction algorithms aim to enhance the quality of life for these individuals by providing timely alerts. This study presents a patient-specific seizure prediction algorithm applied to diverse databases (EPILEPSIAE, CHB-MIT, AES, and Epilepsy Ecosystem). The proposed algorithm undergoes a standardized framework, including data preprocessing, feature extraction, training, testing, and postprocessing. Various databases necessitate adaptations in the algorithm, considering differences in data availability and characteristics. The algorithm exhibited variable performance across databases, taking into account sensitivity, FPR/h, specificity, and AUC score. This study distinguishes between sample-based approaches, which often yield better results by disregarding the temporal aspect of seizures, and alarm-based approaches, which aim to simulate real-life conditions but produce less favorable outcomes. Statistical assessment reveals challenges in surpassing chance levels, emphasizing the rarity of seizure events. Comparative analyses with existing studies highlight the complexity of standardized assessments, given diverse methodologies and dataset variations. Rigorous methodologies aiming to simulate real-life conditions produce less favorable outcomes, emphasizing the importance of realistic assumptions and comprehensive, long-term, and systematically structured datasets for future research.