一种光电容积脉搏信号的峰值点自动识别方法

An Automatic Peak Identification Method for Photoplethysmography Signals

光电容积脉搏信号的峰值点自动识别直接关系到无创血氧饱和度测量与脉搏波峰-峰间期提取的准确率。提出一种小波联合识别方法:基于小波多分辨率分析原理校正影响脉搏波峰值点幅值的基线干扰,再利用二次样条小波模极大算法自动识别峰值点。将该方法应用到自行研制的光电容积脉搏波测量系统中,对采集的信号进行了校正与峰值点识别,通过在信号中增加随机噪声以评价方法的稳定性与可靠性,然后利用10组实测数据,对比本方法与传统差分阈值法的峰值点识别准确率,进一步评价方法的有效性。结果表明:本方法在较好地消除了基线干扰的基础上,在染噪的信号中仍然会较精确地检测出脉搏波主波峰,具有较好的抗干扰能力,有利于提高血氧饱和度检测及峰-峰间期提取的准确性,从而有助于后期人体呼吸功能评价与心率变异性分析。

The automatic recognition of the peak point of the PPG signal is directly related to the accuracy of non-invasive measurement of blood oxygen saturation and the extraction of PP intervals.In this paper,a combined wavelet processing method was proposed.Based on the principle of wavelet multi-resolution analysis,the proposed method corrected the baseline wander which would influence the peak amplitude,and then the peak was identified automatically by using the quadratic spline wavelet modulus maximum algorithm.Using the signals acquired by a self-developed pulse oxygen saturation detecting device to evaluate the effectiveness,the method could correct the baseline wander and identified the peak points of the signal,and to evaluate the stability and reliability of the method,we used a noisy signal.Furthermore,by using ten segments of the measured PPG signals,we compared the peak recognition accuracy of the proposed method with that of a traditional differential threshold method to validate the effectiveness.The results showed that the method not only eliminated the baseline wander,but also could accurately detect the peak of the noisy signal,which had a good anti-jamming capability,and was beneficial to improve the detection of blood oxygen saturation and the accuracy of PP interval extraction.Furthermore,it was helpful to the evaluation of respiratory function and heart rate variability analysis.