Comput Methods Programs Biomed - Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform.


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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 (E(T)) 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 the E(T) and 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 of E(T) can be expressed by multiplying the E(T) by an equivalent window (W(E)). According to the range of heart beats and based on the numerical experiments and the important parameters of the STMHT, a moving window width of N=1s 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 cycle ACC are 100% and 96.69%.

Resumo Limpo

paper propos novel automat method moment segment peak detect analysi heart sound hs pattern special attent characterist envelop hs consid properti hilbert transform ht moment segment peak locat accomplish two step first appli viola integr waveform method time domain envelop et hs signal obtain emphasi first heart sound s second heart sound s base characterist et properti ht convex concav function novel method shorttim modifi hilbert transform stmht propos automat locat moment segment peak point hs zero cross point stmht fast algorithm calcul stmht et can express multipli et equival window accord rang heart beat base numer experi import paramet stmht move window width ns valid locat moment segment peak point hs propos moment segment peak locat procedur method valid sound michigan hs databas sound clinic heart diseas ventricular septal defect vsd aortic septal defect asd tetralog fallot tof rheumat heart diseas rhd result sound s can separ s averag accuraci achiev peak s ap peak s ap moment segment point s s cardiac cycl acc respect sound s separ s averag accuraci achiev peak s s ap cardiac cycl acc

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