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.

翻译：随着动态和长期脑电图（EEG）监测的日益普及，对高质量基于EEG的自动癫痫发作检测算法的需求愈发迫切。这些算法验证方法的异质性影响了所报告的结果，使得全面评估和比较变得困难。这种异质性尤其体现在数据集、评估方法和性能指标的选择上。本文提出一个统一框架，旨在建立基于EEG的癫痫发作检测算法验证的标准化流程。基于现有指南和建议，该框架引入了一套与数据集、文件格式、EEG数据输入内容、癫痫发作标注输入与输出、交叉验证策略及性能指标相关的推荐标准。我们还提出了10-20癫痫发作检测基准——一个基于转换为标准化格式的公开数据集的机器学习基准。该基准定义了机器学习任务及报告指标。我们通过评估一组现有癫痫发作检测算法来展示该基准的应用。SzCORE（癫痫社区开源研究评估）框架和基准与开源软件库一同公开发布，以促进研究使用，同时实现算法临床意义的严格评估，推动集体努力更优地检测癫痫发作，从而改善癫痫患者的生活质量。