基于参数优化VMD的呼吸波提取

Respiratory wave extraction based on parameter optimized VMD

针对目前呼吸波提取准确率不高的问题,提出一种从光电容积脉搏波(PPG)信号中提取呼吸波的改进方法。在MIMIC数据库获取10组脉搏和呼吸信号。利用遗传变异粒子群参数优化的变分模态分解(VMD)算法对同一时段光电容积脉搏信号进行分解,得到本征模函数(IMF),选择相关系数大于0.3的IMF分量重构呼吸信号,并将重构呼吸信号与原始呼吸信号进行比较。实验结果表明,呼吸速率的平均准确率为0.95,波形相关系数(RCC)的平均值为0.9451,均方根误差(RMSE)的平均值为2.0110,该算法提取呼吸波呼吸速率相比于EMD、EEMD算法提高了5%和3%,RCC提高了19.96%和13.17%,准确性更高。同时该算法克服了VMD算法在分解时惩罚因子和分解层数选取的不确定性。这对临床实践具有重要意义。

Aiming at the low accuracy of respiratory wave extraction,an improved method for respiratory wave extraction from photoplethysmography(PPG)signal is proposed.Ten groups of pulse and respiratory signals were obtained from the mimic database.The variation mode decomposition(VMD)algorithm optimized by genetic mutation particle swarm optimization is used to decompose the pulse signal of photo capacitance product in the same period,and the intrinsic mode function(IMF)is obtained.The IMF component with correlation coefficient greater than 0.3 is selected to reconstruct the respiratory signal,and the reconstructed respiratory signal is compared with the original respiratory signal.The experimental results show that the average accuracy of respiratory rate is 0.95,the average value of waveform correlation coefficient(RCC)is 0.9451,and the average value of root mean square error(RMSE)is 2.0110.Compared with EMD and EEMD,the algorithm improves respiratory rate by 5%and 3%,and RCC by 19.96%and 13.17%,with higher accuracy.At the same time,the algorithm overcomes the uncertainty of penalty factor and decomposition level selection in VMD algorithm.This is of great significance to clinical practice.