基于小波变换和希尔伯特包络分析的QRS波检测算法

提出一种基于双正交小波变换和Hilbert变换的QRS波检测算法。首先,通过双正交小波变换分解与重构,消除高频噪声,同时突出R峰位置,构造出有利于QRS波检测的检测层。然后,对信号求差分和希尔波特变换,进一步抑制P波、T波以及基线漂移等噪声。最后,在计算得到的包络信号上根据自适应阈值及决策规则进行R峰检测。根据MIT-BIH心率失常数据库有标注的临床数据进行验证,QRS波检测结果准确率达到99.01%,同时算法具有不错的鲁棒性和实时性。

Detection of QRS Waves Based on Wavelet Transform and Hilbert Envelope Analysis

This paper presents an algorithm for QRS detection using biorthogonal wavelet transform and Hilbert transform. First, this algorithm eliminates the high frequency noise and highlights the R peak position through biorthogonal wavelet transform decomposition and reconstruction, and constructs the detection layer which is beneficial to QRS waves detection. Then a technique with differentiation and Hilbert transform is applied on the re-composed signal in order to suppress the effects of P and T waves and decrease the low frequency noise. Finally, R peak is detected according to the adaptive threshold and decision rules. The MIT-BIH arrhythmia dataset is used to verify the performance of the detection method. The accuracy of QRS wave detection results is 99.01%, and the algorithm has good robustness and real-time performance.