Benchmark of EEG-based seizure detection algorithms with SzCORE

EEG monitoring needs reliable automated seizure detection solutions to aid in the diagnosis and treatment of epilepsy. Many reviews have attempted to catalog and summarize key studies to provide a clear overview of current advances in algorithm development. However, a recurring issue in these reviews is that the field suffers from a lack of standardization in evaluation methodologies and performance metrics, making direct comparisons between algorithms extremely difficult. This work addresses this challenge by providing a fair & transparent comparison review of state-of-the-art seizure detection algorithms in the literature, using a standardized framework: SzCORE (Seizure Community Open-source Research Evaluation). We reviewed the existing literature on patient-independent EEG-based seizure detection algorithms trained on publicly available datasets. We re-implemented some of these algorithms and evaluated them with SzCORE. We found 19 papers that matched our selection criteria. We re-implemented three of them and found notable discrepancies between reported performances and those obtained under standardized evaluation conditions, highlighting the importance of transparent benchmarking. We observed that while algorithms tended to demonstrate high sensitivity (over 90%) in detecting seizure events, they generally exhibited low precision (10–40%), revealing a persistent issue with false-positive rates. We also found a high variability of the computed performance based on evaluation datasets, which is probably partially explained by the hourly rate of seizures. This work shows the value of a standardized evaluation methodology for EEG-based seizure detection and highlights the need for continued algorithm improvements.Clinical relevance— This study highlights the importance of standardized epileptic seizure detection algorithm evaluation. It shows that current state-of-the-art algorithms obtain a high sensitivity (∼70%) at the cost of a low precision (∼14%).