Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform
Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform
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DOI：
10.1016/j.cmpb.2014.02.004
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发表时间：
2014-05
影响因子：
6.1
通讯作者：
Shuping Sun;Zhongwei Jiang;Haibin Wang;Yu Fang
中科院分区：
文献类型：
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作者：
Shuping Sun;Zhongwei Jiang;Haibin Wang;Yu Fang
针对心音包络的特点，结合希尔伯特变换（HT）的性质，提出了一种自动的心音波形矩分割和峰值检测分析方法。矩分割和峰值定位分两步完成。首先，通过在时域中应用Viola积分波形法，以第一心音（S1）和第二心音（S2）为重点，求出HS信号的包络线（ET）。然后，根据变换的特点和凸函数和凹函数的变换性质，提出了一种利用短时修正希尔伯特变换（STMHT）的零交叉点自动定位HS的矩分割和峰值点的新方法。一种计算ET的STMHT的快速算法可以用ET乘以一个等效窗口（WE）来表示。根据心率的变化范围，在数值实验和STMHT重要参数的基础上，确定了N = 1 s的移动窗宽，用于HS的矩分割和峰值点的定位。利用密歇根HS数据库中的声音和临床心脏病（如室间隔缺损（VSD）、主动脉间隔缺损（ASD）、法洛四联症（TOF）、风湿性心脏病（RHD）等）的声音对所提出的矩分割和峰值定位方法进行了验证。对S1峰（AP 1）、S2峰（AP 2）、S1到S2的矩分割点（AT 12）和心动周期（ACC）的平均准确率分别为98.53%、98.31%和98.36%和97.37%。对于S1和S2不能分离的声音，S1和S2峰值（AP 12）和心动周期ACCare的平均准确度分别为100%和96.69%。
This paper proposes a novel automatic method for the moment segmentation and peak detection analysis of heart sound (HS) pattern, with special attention to the characteristics of the envelopes of HS and considering the properties of the Hilbert transform (HT). The moment segmentation and peak location are accomplished in two steps. First, by applying the Viola integral waveform method in the time domain, the envelope (ET) of the HS signal is obtained with an emphasis on the first heart sound (S1) and the second heart sound (S2). Then, based on the characteristics of theETand the properties of the HT of the convex and concave functions, a novel method, the short-time modified Hilbert transform (STMHT), is proposed to automatically locate the moment segmentation and peak points for the HS by the zero crossing points of the STMHT. A fast algorithm for calculating the STMHT ofETcan be expressed by multiplying theETby an equivalent window (WE). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width ofN= 1 s is validated for locating the moment segmentation and peak points for HS. The proposed moment segmentation and peak location procedure method is validated by sounds from Michigan HS database and sounds from clinical heart diseases, such as a ventricular septal defect (VSD), an aortic septal defect (ASD), Tetralogy of Fallot (TOF), rheumatic heart disease (RHD), and so on. As a result, for the sounds where S2 can be separated from S1, the average accuracies achieved for the peak of S1 (AP1), the peak of S2 (AP2), the moment segmentation points from S1 to S2 (AT12) and the cardiac cycle (ACC) are 98.53%, 98.31% and 98.36% and 97.37%, respectively. For the sounds where S1 cannot be separated from S2, the average accuracies achieved for the peak of S1 and S2 (AP12) and the cardiac cycleACCare 100% and 96.69%.