SzCORE:癫痫发作社区开源研究评估框架,用于验证基于脑电图的癫痫发作自动检测算法

IF 6.6 1区 医学 Q1 CLINICAL NEUROLOGY Epilepsia Pub Date : 2024-09-18 DOI:10.1111/epi.18113
Jonathan Dan, Una Pale, Alireza Amirshahi, William Cappelletti, Thorir Mar Ingolfsson, Xiaying Wang, Andrea Cossettini, Adriano Bernini, Luca Benini, Sándor Beniczky, David Atienza, Philippe Ryvlin
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引用次数: 0

摘要

随着非卧床和长期脑电图(EEG)监测应用的不断增加,对基于脑电图(EEG)的高质量癫痫发作自动检测算法的需求日益迫切。这些算法验证方法的不一致性影响了报告结果,并使全面评估和比较具有挑战性。这种异质性尤其涉及数据集、评估方法和性能指标的选择。在本文中,我们提出了一个统一的框架,旨在建立基于脑电图的癫痫发作检测算法验证的标准化。在现有指南和建议的基础上,该框架引入了一套与数据集、文件格式、脑电图数据输入内容、癫痫发作注释输入和输出、交叉验证策略和性能指标相关的建议和标准。我们还提出了 EEG 10-20 癫痫发作检测基准,这是一个基于转换为标准化格式的公共数据集的机器学习基准。该基准定义了机器学习任务以及报告指标。我们通过评估一组现有的癫痫发作检测算法来说明基准的使用。SzCORE(癫痫发作社区开源研究评估)框架和基准与一个开源软件库一起公开发布,以方便研究使用,同时对算法的临床意义进行严格评估,促进集体努力,以更优化的方式检测癫痫发作,改善癫痫患者的生活。
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SzCORE: Seizure Community Open‐Source Research Evaluation framework for the validation of electroencephalography‐based automated seizure detection algorithms
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 EEG 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.
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来源期刊
Epilepsia
Epilepsia 医学-临床神经学
CiteScore
10.90
自引率
10.70%
发文量
319
审稿时长
2-4 weeks
期刊介绍: Epilepsia is the leading, authoritative source for innovative clinical and basic science research for all aspects of epilepsy and seizures. In addition, Epilepsia publishes critical reviews, opinion pieces, and guidelines that foster understanding and aim to improve the diagnosis and treatment of people with seizures and epilepsy.
期刊最新文献
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