在动态条件下对增强的SCG信号处理的Denoising算法的比较评估
概括
这项研究通过比较denoising算法来增强使用地震心脏图 (SCG) 的心脏监测. 变化模式分解和萨维茨基-戈莱过显著提高了运动期间的心率的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 心血管监测 心血管监测
- 信号处理 信号处理
背景情况:
- 地震心脏图 (SCG) 提供非侵入性心脏监测,非常适合可穿戴设备.
- 在SCG信号中的运动工件,特别是在运动期间,损害了数据可靠性.
- 在动态条件下准确估计心率仍然是SCG的一个挑战.
研究的目的:
- 为了比较评估SCG信号的各种无声化算法.
- 为了评估 denoising 在动态活动期间对心率估计准确性的影响.
- 确定最佳的信号处理技术,以实现基于SCG的强大监控.
主要方法:
- 研究了七种否定方法:EMD,EEMD,CEEMD,VMD,Savitzky-Golay,移动平均和波形分解.
- 采用了四种心率估计方法:峰值检测,包裹和Teager-Kaiser能量运算器.
- 收集了20名参与者休息和步行运动期间使用可穿戴贴片的SCG数据.
主要成果:
- 变化模式分解 (VMD) 和Savitzky-Golay过,加上包裹,产生了最佳的性能.
- 与未处理的信号相比,这些方法在运动期间将心率估计误差 (MAPE和RMSE) 降低了高达38%.
- 在动态条件下显著改善SCG信号质量和心率精度.
结论:
- 先进的消毒技术对于在门诊环境中基于SCG的可靠心率监测至关重要.
- VMD和Savitzky-Golay过显示出增强体力活动期间SCG应用的希望.
- 改进的SCG信号处理支持在现实场景中持续,准确的心血管评估.
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