一种基于稳定性和聚合的方法,用于估计心率,使用在体力活动期间的光电脉学信号
Sabrina C Crepaldi1, Jiabin Wang1, Fumiya Matsumoto2
1SOXAI Inc., Kanagawa 231-0032, Japan.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
一种新的基于聚合的方法可以准确地估计体育活动期间从光电显微镜 (PPG) 信号中心率. 这种方法在没有深度学习的情况下最大限度地减少了运动工件,为可穿戴健康监测提供了实用,经济高效的解决方案.
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:
- 光电心电图 (PPG) 是一种成本效益高的心率监测方法,可以替代心电图 (ECG).
- 对于PPG分析而言,深度学习需要大量的数据和计算资源,这限制了现实世界的应用,特别是没有基本真相的情况下.
- 运动工件在体力活动期间显著降低了PPG信号质量.
研究的目的:
- 开发一种计算效率高,适合所有人的一种方法,用于在体育活动期间从PPG准确估计心率.
- 在不依赖复杂的机器学习或深度学习模型的情况下,最大限度地减少运动工件的影响.
- 为了证明信号处理技术在匹配深度学习性能方面的有效性.
主要方法:
- 基于聚合的信号处理方法被用于心率跟踪.
- 该方法旨在最大限度地减少运动工件对PPG信号的影响.
- 对多个公共数据集 (PPG-DaLiA,WESAD,IEEE) 和一个新的智能环数据集 (UTOKYO) 进行了评估.
主要成果:
- 拟议的方法在PPG-DaLiA和IEEE_Test数据集上与CNN组合相比显示出更高的性能.
- 在这些数据集中,平均绝对误差 (MAE) 分别减少了1.45 bpm和5.71 bpm.
- 该方法实现了高精度,而不需要大量的计算资源或数据集特定的调整.
结论:
- 有效的信号处理技术可以在体力活动期间从PPG提供准确的心率估计.
- 开发的方法为可穿戴医疗设备提供了对深度学习的实用和资源高效的替代方案.
- 这种方法克服了深度学习的局限性,例如数据依赖性和计算成本.
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