一种基于多特征的P波检测方法  

A P-wave Detection Method Based on Multi-feature

作　　者：宋立新[1] 关丽丽[1] 王乾[2] 王宇虹[1] 

机构地区：[1]哈尔滨理工大学电气与电子工程学院,哈尔滨150080 [2]哈尔滨理工大学计算中心,哈尔滨150080

出　　处：《生物医学工程学杂志》2014年第2期283-287,共5页Journal of Biomedical Engineering

基　　金：黑龙江省自然科学基金资助项目(F200912);哈尔滨创新人才基金资助项目(2010RFXXS026)

摘　　要：由于P波一般为低频低幅波,容易受到基线漂移,肌电干扰等噪声影响,且不是每个心拍都包含P波,确定某一心拍有无P波也是一难题,针对小波-幅值-斜率的方法对多样形态P波适应的局限性,以及小波变换结合神经网络检测方法中选取伪P波样本的局限性,本文提出了基于小波-幅值阈值并以多特征作为神经网络的输入的P波检测方法,该方法首先利用小波变换对心电(ECG)信号进行去噪,然后利用小波变换求模极大值对的方法确定候选P波的位置,接下来利用幅值阈值初步判断有无P波,最后利用神经网络确定心拍有无P波。本文经由专家注释的QT心电数据库对该算法和传统的小波阈值法及基于小波-幅值-斜率的方法检测ECG信号P波的效果进行了对比,验证了本文提出的算法的可行性,对医院心电科记录的ECG信号进行了检测,其结果与医生的标注基本相同,并对QT数据库中的13份且每份15min的ECG信号进行了检测验证,P波正确检测率达到了99.911%。Generally, P-wave is the wave of low-frequency and low-amplitude, and it could be affected by baseline drift, electromyography （EMG） interference and other noises easily. Not every heart beat contains the P-wave, and it is also a major problem to determine the P-wave exist or not in a heart beat. In order to solve the limitation of suit- ing the diverse morphological P-wave using wavelet-amplitude-transform algorithm and the limitation of selecting the pseudo-P-wave sample using the wavelet transform and neural network, we presented new P-wave detecting method based on wave-amplitude threshold and using the multi-feature as the input of neural networks. Firstly, we removed the noise of ECG through the wavelet transform, then determined the position of the candidate P-wave by calculating modulus maxima of the wavelet transform, and then determine the P-wave exist or not by wave-amplitude threshold method initially. Finally we determined whether the P-wave existed or not by the neural networks. The method is validated based on the QT database which is supplied with manual labels made by physicians. We compared the de- tection effect of ECG P-waves, which was obtained with the method developed in the study, with the algorithm of wavelet threshold value and the method based on ＂wavelet-amplitude-slope＂ , and verified the feasibility of the pro- posed algorithm. The detected ECG signal, which is recorded in the hospital ECG division, was consistent with the doctor＇s labels. Furthermore, after detecting the 13 sets of ECG which were 15rain long, the detection rate for the correct P-wave is 99. 911%.

关 键 词：心电信号 小波变换 神经网络 P波检测 

分 类 号：TN911.7[电子电信—通信与信息系统]