基于归一化最小均方差联合集合经验模态分解的运动状态下心率提取算法研究

Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition

为了消除运动伪差、高频噪声和基线漂移对光电容积脉搏波(PPG)的影响,得到运动状态下心率的准确值,本文提出了一种基于归一化最小均方差(NLMS)自适应滤波器联合集合经验模态分解(EEMD)分析的PPG信号降噪方法。首先,将含有噪声的PPG信号以3轴加速度传感器为参考信号通过自适应滤波器,滤除其中的运动伪差;其次,将PPG信号通过EEMD分解得到一系列按频率由高到低的固有模态分量(IMF),通过排列熵(PE)准则判断信号的阈值范围,从而滤除其中的高频噪声和基线漂移。实验结果显示,不同运动状态下,降噪后PPG信号的计算心率和基于心电信号(ECG)的标准心率的皮尔逊相关系数为0.731,平均绝对误差百分比为6.10%,从而表明该方法能够准确计算出运动状态的心率,有利于人体运动状态下的生理监测。

In order to eliminate the influence of motion artifacts,high-frequency noise and baseline drift on photoplethysmographic(PPG),and to obtain the accurate value of heart rate in motion state,this paper proposed a denoising method of PPG signal based on normalized least mean square(NLMS)adaptive filtering combining ensemble empirical mode decomposition(EEMD).Firstly,the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts.Secondly,the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function(IMF)according to the frequency from high to low.The threshold range of the signal is judged by the permutation entropy(PE)criterion,thereby filtering out the high frequency noise and the baseline drift.The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram(ECG)signal is 0.731 and the average absolute error percentage is 6.10%under different motion states,which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.