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在现实世界的可穿戴环境中基于心电图检测发作:来自SeizeIT2数据集的见解

Conrad Reintjes¹, Janosch Fabio Hagenbeck¹, Mohamed Ballo¹

¹Cologne Institute for Information Systems, University of Cologne, 50923 Cologne, Germany.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

这项研究评估了可穿戴式心电图 (ECG) 发作检测方法在一个大数据集. 时间VQVAE-AD显示高灵敏度,而马德里有较少的错误报警,突出了监测的关键权衡.

科学领域:

神经学神经学
生物医学工程生物医学工程
数据科学数据科学数据科学

背景情况:

症管理需要精确的发作追踪,但目前的自我报告方法不可靠.
基于可穿戴传感器的解决方案提供了客观的替代方案,心电图 (ECG) 显示了发作检测的希望.
之前的ECG发作检测研究受到小数据集的限制.

研究的目的:

评估使用心电图数据检测发作的三个异常检测算法 (矩阵配置文件,MADRID,TimeVQVAE-AD) 的性能.
为了在SeizeIT2上对这些方法进行比较,SeizeIT2是最大的公开可用的可穿戴心电图数据集.
为了建立一个可复制的基线,为未来的研究,基于可穿戴的发作检测.

主要方法:

使用了SeizeIT2数据集,包括11640小时的可穿戴心电图记录和886次注释性发作.
应用标准化预处理技术和临床相关的窗口策略.
基于灵敏度,错误报警率 (FAR) 和和平均分数的基准算法.

主要成果:

时间VQVAE-AD显示了最高的发作检测灵敏度.
马德里实现了最低的虚假警报率,表明虚假警报较少.
在最大限度地提高发作检测灵敏度和最大限度地减少虚假警报之间观察到一个明确的权衡.

结论:

在SeizeIT2数据集上基于心电图的异常检测可以达到临床相关的灵敏度水平.
这项研究强调了在心电图发作检测中固有的灵敏度与错误报警的权衡.
可重现的基准和代码的发布有助于进一步研究个性化和临床适用的监测解决方案.

关键词:

检测异常检测异常检测电心电图 (ECG) 是一种心电图.是一种.机器学习是机器学习.发作检测检测发作检测可以穿戴的传感器.

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