基于强大的R峰检测模型诱导的HRV特征的运动心电图分类
Xinhua Su1, Xuxuan Wang1, Huanmin Ge1
1School of Sports Engineering (China Big Data Center for Sports), Beijing Sport University, Beijing, China.
Computer methods in biomechanics and biomedical engineering
|February 20, 2026
概括
本研究介绍了UNet-M-D用于在噪音较大的运动心电图 (ECG) 中精确检测R峰值,改进了运动健康管理的疲劳分类.
科学领域:
- 运动科学 运动科学 运动科学
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
背景情况:
- 在运动心电图 (ECG) 中精确检测R峰值对于通过心率变化 (HRV) 分析来评估运动引起的疲劳至关重要.
- 现有的模型在身体活动期间记录的ECG固有的噪音中扎.
- 开发强大的R峰检测方法对于体育中可靠的疲劳评估至关重要.
研究的目的:
- 开发和评估一种新的深度学习模型,UNet-M-D,用于在杂的运动心电图信号中精确检测R峰值.
- 评估拟议模型在提高运动诱导疲劳分类准确度方面的有效性.
- 根据客观疲劳指标,为增强体育健康管理和训练调整提供基础.
主要方法:
- 拟议的UNet-M-D模型整合了定位编码,多头自我注意力和动态卷积,以增强R峰检测.
- 模型评估使用GUDB和EPFL心电图数据集,这些数据集已知含有运动引起的噪声.
- 从检测到的R-峰值推导出的心率变化 (HRV) 度量中选择特征,用于随后的疲劳分类.
主要成果:
- UNet-M-D实现了卓越的R峰检测精度,在评估的数据集上达到高达99.2%.
- 该模型表现出显著的噪声弹性,在信号噪声比率 (SNR) 中表现良好,低至6-18dB.
- 疲劳分类准确度达到77.4%,使用从UNet-M-D R-peak检测中获得的最佳选择的HRV特征.
结论:
- 在具有挑战性的运动心电图条件下,UNet-M-D模型为R峰检测提供了强大而准确的解决方案.
- 改进的R峰检测直接转化为更可靠的HRV特征提取和随后的疲劳分类.
- 这项研究为客观体育健康监测和个性化训练方案优化提供了有价值的工具.
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