基于单通道心电图信号的睡眠和清醒阶段分类,使用动态连接卷积神经网络的动态连接
Junming Zhang1,2,3,4, Hao Dong3, Yipei Li5
1School of Computer and Artificial Intelligence, Huanghuai University, Zhumadian, Henan, China.
Computer methods in biomechanics and biomedical engineering
|February 17, 2025
概括
这项研究引入了一种使用心电图 (ECG) 信号的新型睡眠-清醒阶段分类模型,达到92.21%的准确性. 这种方便的方法非常适合可穿戴睡眠监测设备.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 准确的睡眠-清醒阶段识别对于评估睡眠质量至关重要.
- 目前的方法通常依赖于电脑电图 (EEG) 信号,这些信号不方便,易受噪声的影响.
- 电心电图 (ECG) 信号为睡眠监测提供了一个更简单,更方便的替代方案.
研究的目的:
- 使用心电图信号开发一种简单有效的睡眠和清醒阶段分类模型.
- 通过可穿戴设备实现睡眠监测.
- 克服基于EEG的方法的局限性.
主要方法:
- 使用不同尺寸的卷积内核提取多尺度的ECG信号特征.
- 一个新的动态连接卷积神经网络 (DCCNN) 的建议,用于分类.
- DCCNN利用层特征地图"善良"来形成最佳的残余模块.
主要成果:
- 拟议的DCCNN模型在麻省理工学院的多睡眠数据库中实现了92.21%的峰值准确性.
- 性能与传统基于EEG的睡眠和清醒分类方法相提并论,并且往往优于它们.
- 与现有的最先进的技术相比,这种方法显示出显著的有效性.
结论:
- 这项研究提出了一种使用心电图信号进行睡眠监测的新,有效和方便的方法.
- DCCNN模型为可穿戴睡眠跟踪应用提供了一个有希望的替代方案.
- 这些发现支持基于心电图的分析对睡眠医学的潜力.
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