The need for high-quality automated seizure detection algorithms based on
electroencephalography (EEG) becomes ever more pressing with the increasing use
of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods
of these algorithms influences the reported results and makes comprehensive
evaluation and comparison challenging. This heterogeneity concerns in
particular the choice of datasets, evaluation methodologies, and performance
metrics. In this paper, we propose a unified framework designed to establish
standardization in the validation of EEG-based seizure detection algorithms.
Based on existing guidelines and recommendations, the framework introduces a
set of recommendations and standards related to datasets, file formats, EEG
data input content, seizure annotation input and output, cross-validation
strategies, and performance metrics. We also propose the 10-20 seizure
detection benchmark, a machine-learning benchmark based on public datasets
converted to a standardized format. This benchmark defines the machine-learning
task as well as reporting metrics. We illustrate the use of the benchmark by
evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure
Community Open-source Research Evaluation) framework and benchmark are made
publicly available along with an open-source software library to facilitate
research use, while enabling rigorous evaluation of the clinical significance
of the algorithms, fostering a collective effort to more optimally detect
seizures to improve the lives of people with epilepsy.

通过提出一个统一的框架和机器学习基准，该研究旨在标准化基于脑电图的癫痫检测算法的验证方法，并评估一组现有算法，以改善癫痫患者的生活。