Artifact Removal from Data Generated by Nonlinear Systems: Heart Rate Estimation from Blood Volume Pulse Signal
Artifact Removal from Data Generated by Nonlinear Systems: Heart Rate Estimation from Blood Volume Pulse Signal
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DOI：
10.1021/acs.iecr.9b04824
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发表时间：
2020-02-12
影响因子：
4.2
通讯作者：
Cinar, Ali
中科院分区：
文献类型：
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作者：
Askari, Mohammad Reza;Rashid, Mudassir;Cinar, Ali
伪影是影响测量信号的干扰，这些干扰不是源自过程本身。本文解决了光电容积脉搏波（PPG）传感器的心率（HR）监测问题，其中由身体运动引起的伪影影响测量信号的质量。通过使用奇异谱分析来处理PPG信号以减少信号损坏。为了消除伪影，伪影相关的三维加速度计信号被用作辅助信号，并提出了一种新的谱减法去除伪影。将净化后的信号加窗成连续的时间段，并且对于经处理的信号和加速度计数据的每个时间窗，执行特征提取。在各种类型的身体活动期间从心电图传感器获得地面真实HR值，以捕获广泛的HR变化。使用递归神经网络从提取的特征和实际心率值构建模型，以从PPG信号估计HR。
Artifacts are disturbances affecting a measured signal that are not originating from the process itself. This paper addresses the problem of heart rate (HR) monitoring from a photoplethysmography (PPG) sensor, where artifacts caused by body movements affect the quality of the measurement signal. The PPG signal is processed by using the singular spectrum analysis to reduce signal corruption. To remove the artifacts, the artifact-correlated three-dimensional accelerometer signal is used as the auxiliary signal, and a novel spectral subtraction approach is proposed for artifact removal. The cleaned signal is windowed into consecutive time segments, and for each time window of the processed signal and accelerometer data, feature extraction is performed. Ground-truth HR values are obtained from an electrocardiograph sensor during various types of physical activities to capture a broad range of HR variations. A recurrent neural network is used to build a model from extracted features and actual heart rate values to estimate HR from PPG signal.