针对光电容积脉搏波(Photoplethysmography,PPG)传感器数据采集降噪问题,本文提出一种基于联合稀疏重构的PPG信号运动噪声降噪算法.该算法通过构建同时间段内PPG信号和加速度信号的频谱矩阵,提取频谱矩阵稀疏特征和该矩阵行稀疏特征,利用压缩感知方法,将PPG信号运动噪声去除过程建模为联合稀疏信号重构过程,并将该过程进一步建模为最优化模型,通过迭代寻优来获得该模型的最优解,结合谱减法,从而有效去除PPG信号中的运动噪声,降低噪声对PPG信号的影响.仿真分析表明,本文提出的算法能有效去除PPG信号中的运动噪声,获得较好的降噪效果.%This paper proposes a joint sparse spectrum reconstruction-based motion artifact reduction algorithm for Photoplethysmo-graphy (PPG) signals to overcome the artifact removing problem in the PPG sensor data collection.Firstly,our algorithm constructs a spectral matrix,using PPG signals and acceleration signals during the same time period.The sparse characteristics of the spectral matrix and its rows are extracted.Secondly,we use the compressive sensing to model the motion artifact removing process in PPG signals as a joint sparse signal reconstruction process.Then this process is further modeled as an optimal model.We exploit the iterative method to obtain the optimal solution to the model.Finally,we combine the spectrum subtraction to remove the motion artifact in PPG signals.In the result,we can effectively decrease the impact of the motion artifact on PPG signals.Simulation results demonstrate that the algorithm proposed in this paper can effectively remove the motion artifact in PPG signals and attain the better noise reduction performance.
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