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在地震心电图中使用基于心电图算法的自动心率检测 - - 一项可行性研究
Evgenii Pustozerov1, Ulf Kulau2, Urs-Vito Albrecht1
1Department of Digital Medicine, Medical Faculty OWL, Bielefeld University, 33615 Bielefeld, Germany.
Bioengineering (Basel, Switzerland)
|June 27, 2024
概括
电心电图 (ECG) 信号的算法可以适应地震心电图 (SCG) 信号来计算心率 (HR). 这项研究验证了这些方法,显示了SCG在现实世界的人力资源监测中的前景.
科学领域:
- 生物医学工程 生物医学工程
- 心血管生理学心血管生理学
- 信号处理 信号处理
背景情况:
- 心率 (HR) 分析传统上依赖心电图 (ECG) 信号.
- 地震心脏图 (SCG) 为监测心脏活动提供了一个非侵入性的替代方案.
- 为ECG开发的现有HR算法为SCG信号分析提供了潜在的途径.
研究的目的:
- 为了研究ECG衍生R峰检测算法的适用性,用于使用SCG信号进行HR计算.
- 评估和优化基于SCG的HR估计的预处理和峰值检测方法.
- 为了比较基于SCG的HR检测精度与ECG和光斑显微镜 (PPG) 方法.
主要方法:
- 调整了SCG信号的已建立的ECG信号处理和峰值检测算法.
- 在CEBS数据集上系统评估了15种峰值检测和6种预处理方法.
- 通过实验室收集的SCG数据在休息和体力活动期间验证了表现最佳的方法.
主要成果:
- 选择的方法在坐着休息期间从SCG检测HR的高精度 (与ECG的0.12±0.35bpm差异).
- 在体力活动期间观察到适度的精度 (6.45 ± 3.01 bpm与心电图的差异).
- 性能与文献中最先进的光聚血学 (PPG) 方法相当.
结论:
- 电脑简单的预处理和ECG的峰值检测技术对于SCG的HR检测是可行的.
- SCG显示了HR监控的潜力,特别是在休息条件下.
- 需要进一步改进,以提高体力活动期间SCG HR检测准确度.
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