基于小波变换的心电图信号特征点检测

Electrocardiosignal Feature Extraction Based on Wavelet Transform

心电图信号特征点的检测是心电自动诊断技术的基础。利用小波变换多分辨分析对心电信号去噪,利用小波变换逼近信号滤除基线漂移,采用默认阈值处理滤除高频噪声,并且将心电信号的平均值置为0。在R峰检测中,针对单独考虑23细节信号会出现漏检的情况,综合考虑23和24两个尺度上的细节信息,能够有效地防止漏检。提出有效防止误检的方法,并取得了良好的效果。分别采用面积法、时间电压法、幅值法计算平均心电轴,比较结果发现,采用面积法具有精度高的优点。

Electrocardiosignal feature extraction is the base of eleetrocardiologic automatic diagnosis. By using wavelet transform muhi- resolution analysis, the noise in electrocardiosignal is removed ; and by using proximity signals of wavelet transform the baseline wander is filtered. The high frequency noise is handled and eliminated with the default threshold ; and the average value of the electrocardiosignals is set to zero. In detection of R peak, because leak detection will occur when only 23 detail signals is considered, thus the 23 and 24detail signals are integrated to avoid miss detection effectively. The methods avoiding error detection bring excellent effects. For calculating average cardiac electric axis, among the methods of area method, time-voltage method and amplitude method, the area method offers the highest accuracy.