A Novel Motion Artifact Removal Method via Joint Basis Pursuit Linear Program to Accurately Monitor Heart Rate
A Novel Motion Artifact Removal Method via Joint Basis Pursuit Linear Program to Accurately Monitor Heart Rate
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DOI：
10.1109/jsen.2019.2927994
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发表时间：
2019-11-01
影响因子：
4.3
通讯作者：
Du, Dongping
中科院分区：
文献类型：
--
作者：
Koneshloo, Amirhossein;Du, Dongping
在体育锻炼期间基于光电体积描记（PPG）的心率（HR）估计是具有挑战性的，因为PPG信号通常被运动伪影（MA）污染。本研究开发了一种新的HR估计方法，以有效地减弱MA对PPG信号的影响，并准确地识别体育锻炼期间的HR变化。首先，基于联合基追踪线性规划（BPLP）实现了一种新的信号重构过程，以将PPG信号分解为不同的时间序列。此外，自适应MA去除技术的开发，其中加速度信号和PPG时间序列之间的相关性的计算和使用作为参考，以消除MA。然后，设计了一种新的稀疏谱重构方法，利用前一帧数据重构当前窗口的频谱。此外，一个简单的HR估计方法，只有一个调谐参数的设计，以选择HR相关的峰从重建的光谱。最后，应用后处理技术进一步提高检测的准确性。使用2015年IEEE信号处理杯的训练集和测试集，将该算法的性能与最近研究中的三种流行方法进行了比较。所提出的方法在所有22个记录上提供了1.79次每分钟心跳（BPM）的平均绝对误差。对于具有较强MA的测试数据集，平均绝对误差计算为2.61BPM。所提出的HR跟踪算法显示出良好的鲁棒性，因为它只涉及一小部分参数，并且当PPG信号被强MA污染时可以提供准确的估计。
Photoplethysmography (PPG)-based heart rate (HR) estimation during physical exercise is challenging as PPG signals are often contaminated by motion artifacts (MA). This study develops a novel HR estimation method to effectively attenuate the impact of MA on PPG signals and accurately identify HR variations during physical exercise. First, a new signal reconstruction procedure is implemented based on a joint basis pursuit linear program (BPLP) to decompose PPG signal into different time series. Furthermore, an adaptive MA removal technique is developed, where the correlation between the acceleration signals and PPG time series are calculated and used as a reference to eliminate MA. Then, a new sparse spectra reconstruction method is designed to rebuild the spectrum of the current window based on the previous time frame. Furthermore, a simple HR estimation method with only one tuning parameter is designed to select the HR associated peak from the reconstructed spectra. Finally, a postprocessing technique is applied to further boost the accuracy of detection. The performance of the proposed algorithm is compared with three popular methods in recent studies using both training and testing sets from 2015 IEEE Signal Processing Cup. The proposed method provides the average absolute error of 1.79 beats per minutes (BPM) on all 22 recordings. With respect to testing datasets with stronger MA, the average absolute error is computed as 2.61BPM. The proposed HR tracking algorithm shows good robustness as it only involves a small set of parameters and can provide accurate estimations when PPG signals are contaminated by strong MA.