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评估DS-EWMA算法的脉冲率估计性能
概括
基于主导分数和指数加权移动平均数驱动 (DS-EWMA) 算法在估计从具有挑战性的光聚光学 (PPG) 信号的脉冲率方面表现强,在运动期间或使用基于部传感器的其他方法中表现优于其他方法.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 医疗信息学 医疗信息学
背景情况:
- 可靠的生命体征监测,特别是心率估计,对于医疗保健至关重要.
- 光电显微镜 (PPG) 信号被广泛用于生命体征监测,特别是在家庭环境中.
- 基于PPG的生命体征估计的性能在很大程度上取决于所选择的算法和传感器位置.
研究的目的:
- 为了评估DS-EWMA脉冲率估计算法的稳定性和性能.
- 将DS-EWMA与使用多种PPG数据集的六种最先进的算法进行比较.
- 在不同的条件下评估算法性能,包括静止和运动诱导的工件.
主要方法:
- 基于主导分数和指数加权移动平均数驱动 (DS-EWMA) 的算法被评估.
- 对比涉及六种已建立的算法:HeartPy,BioSPPy,Msptd,Qppg,Ampd和Erma. 这三种算法都被使用了.
- 通过使用RMSE,MAE,STD,PCC,MAPE等指标以及四个PPG数据集的百分比准确度来评估性能.
主要成果:
- 在静止条件下,手指PPG,HeartPy和Msptd通常表现出优异的性能.
- 在静止条件下,DS-EWMA,Ampd和Erma表现中等.
- 在分析杂的PPG信号 (部PPG,受运动影响的手指PPG) 时,DS-EWMA显著优于其他算法.
结论:
- DS-EWMA算法表现出显著的稳定性,特别是在具有挑战性的PPG信号场景中.
- 像HeartPy,Qppg和BioSPPy这样的现有算法显示出运动工件或非理想传感器位置的限制.
- 进一步完善DS-EWMA方法有望提高生命体征监测准确度.
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