NDLI: Automatic segmentation of the second cardiac sound by using wavelets and hidden Markov models

Content Provider	IEEE Xplore Digital Library
Author	Lima, C. S. Barbosa, D.
Copyright Year	2008
Description	Author affiliation: University of Minho, Industrial Electronics Department, Campus de Azurém 4800-058 Gimarães, Portugal (Lima, C. S.; Barbosa, D.)
Abstract	This paper is concerned with the segmentation of the second heart sound (S2) of the phonocardiogram (PCG), in its two acoustic events, aortic (A2) and pulmonary (P2) components. The aortic valve (A2) usually closes before the pulmonary valve (P2) and the delay between these two events is known as “split” and is typically less than 30 miliseconds. S2 splitting, reverse splitting or reverse occurrence of components A2 and P2 are the most important aspects regarding cardiac diagnosis carried out by the analysis of S2 cardiac sound. An automatic technique, based on discrete wavelet transform and hidden Markov models, is proposed in this paper to segment S2, to estimate de order of occurrence of A2 and P2 and finally to estimate the delay between these two components (split). A discrete density hidden Markov model (DDHMM) is used for phonocardiogram segmentation while embedded continuous density hidden Markov models are used for acoustic models, which allows segmenting S2. Experimental results were evaluated on data collected from five different subjects, using CardioLab system and a Dash family patient monitor. The ECG leads I, II and III and an electronic stethoscope signal were sampled at 977 samples per second.
Starting Page	334
Ending Page	337
File Size	217802
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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