基于运动噪声构造结合凸组合最小均方自适应滤波算法检测动态血氧饱和度的研究

Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter

基于光电检测的运动血氧饱和度(血氧)检测仪其性能在很大程度上受到光电容积脉搏波(PPG)信号中运动噪声的影响。本文提出一个针对运动血氧检测的算法,该算法通过完全自适应噪声集合经验模态分解联合多尺度排列熵构造运动噪声参考信号,并结合凸组合最小均方自适应滤波器消除PPG信号中的运动噪声,以计算动态血氧。测试结果表明,在模拟步行、慢跑状态下,本算法计算的血氧值与参考血氧值的平均绝对误差分别为0.05和0.07,平均绝对百分比误差分别为0.05%和0.07%,总体皮尔逊相关系数达到0.9712。实验结果证实,本文研究算法有效地降低了PPG信号中的运动伪影,有望应用于便携光电式脉搏血氧仪,以提高动态血氧测量的准确性。

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise(MA)in the photoplethysmographic(PPG)signal.This paper presents an algorithm for detecting motion oxygen saturation,which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy,and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation.The test results show that,under simulated walking and jogging conditions,the mean absolute error(MAE)of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07,respectively,with means absolute percentage error(MAPE)of 0.05%and 0.07%,respectively.The overall Pearson correlation coefficient reaches 0.9712.The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.