基于运动损坏的光电质量学对心率估计的开源算法的基准测试
Marcello Sicbaldi1, Luca Palmerini2, Serena Moscato1
1Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, 40136, Italy.
Computers in biology and medicine
|July 27, 2025
概括
深度学习算法集成的光聚体显微镜 (PPG) 和加速度计数据显著提高心率监测的准确性,特别是在运动工件. 在现实世界中,BeliefPPG算法在现实条件下表现出卓越的性能.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 信号处理 信号处理
背景情况:
- 光电脉扫描 (PPG) 能够通过可穿戴设备进行非侵入性心率监测.
- 运动器件经常会损害基于PPG的心率估计的可靠性.
- 加速度计数据集成是缓解PPG信号中运动工件的关键策略.
研究的目的:
- 系统地评估和基准公布的算法,结合PPG和加速度计数据用于心率估计.
- 为了确定表现最好的算法,特别是在动态条件下与运动工件.
- 将深度学习方法与传统基于模型的方法进行比较.
主要方法:
- 一个全面的文献搜索确定了126个相关的算法.
- 11个开源算法使用标准化现实数据集进行了基准测试.
- 应用了一个强大的方法框架,详细的绩效指标.
主要成果:
- 深度学习算法始终优于基于模型的方法和不使用加速度计数据的方法.
- BeliefPPG算法在所有指标中显示出最佳表现.
- 信念PPG实现了较低的估计偏差 (0.7 ± 0.8 bpm) 和可变性 (4.4 ± 2.0 bpm),与基本真相有很强的相关性 (0.73 ± 0.14 bpm).
结论:
- 与加速度计数据集成的深度学习技术显著提高了基于PPG的心率监测的可靠性.
- BeliefPPG算法代表了在现实世界中准确估计心率的最先进解决方案.
- 未来的研究应该专注于开发多样化的数据集,以提高算法概括性.
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