从使用卷积神经网络的电肌图信号评估心律和心律失常检测
M A Sazhina1, S A Lobov2, A S Pimashkin3
1Research Engineer, Engineering Center; National Research Lobachevsky State University of Nizhny Novgorod, 23 Prospekt Gagarina, Nizhny Novgorod, 603022, Russia.
Sovremennye tekhnologii v meditsine
|February 16, 2026
概括
一个新的算法使用电肌图 (EMG) 信号来评估心律,从而实现非侵入性心脏监测. 这项技术提供了一种具有成本效益的方法,可以使用可穿戴的EMG系统来查心律失常.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 可穿戴技术可穿戴技术
背景情况:
- 表面电肌图 (EMG) 系统通常用于监测肌肉活动.
- 评估心律通常需要心电图 (ECG).
- 需要综合系统进行非侵入性功能状态监测,包括心律不整.
研究的目的:
- 开发和验证使用EMG信号进行心律评估的卷积自编码算法.
- 探索MyosuitEMG系统用于心脏监测的潜力.
- 为了使全面的功能状态评估和心律失常查.
主要方法:
- 开发了一个完全卷积式的自动编码器,在R波面具上训练.
- 记录了6名健康男性的同步EMG,ECG和心率监测器数据.
- 在休息和运动期间使用F-score在聚合,离开一个受试者和个人受试者场景中评估模型性能.
主要成果:
- 卷积自编码器成功地从EMG信号中提取了节律图.
- 实现了高分类表现,特别是在离开一个主题的场景中,这表明了良好的概括性.
- 证实了使用Myosuit EMG电极同时监测肌肉和心率的可行性.
结论:
- 开发的算法可以可靠地从EMG信号中提取节律图,用于心率变化分析和心律失常查.
- Myosuit EMG 系统与算法相结合,为非侵入性心脏监测提供了一种可行的方法.
- 这为具有成本效益的可穿戴系统铺平了道路,可以在没有专用心电图通道的情况下实时查心律失常.
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