Comput. Biol. Med. - Noninvasive detection of mechanical prosthetic heart valve disorder.

Tópicos

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Resumo

Auscultation is a widely used efficient technique by cardiologists for detecting the heart conditions. Since the mechanical prosthetic heart valves are widely used today, it is important to develop a simple and efficient method to detect abnormal mechanical valves. In this paper, the mechanical prosthetic heart valve sounds are analyzed by using different power spectral density (PSD) estimation techniques. To improve the classification accuracy of heart sounds, we propose two different feature extraction schemes, i.e., a modified local discriminant bases (LDB) scheme and a Hilbert-Huang Transform (HHT)-based scheme. A database of 150 heart sounds is used in this study and an average classification accuracy of 97.3% is achieved for both the two feature extraction schemes, when a generic linear discriminant analysis (LDA) classifier is used in the classification stage.

Resumo Limpo

auscult wide use effici techniqu cardiologist detect heart condit sinc mechan prosthet heart valv wide use today import develop simpl effici method detect abnorm mechan valv paper mechan prosthet heart valv sound analyz use differ power spectral densiti psd estim techniqu improv classif accuraci heart sound propos two differ featur extract scheme ie modifi local discrimin base ldb scheme hilberthuang transform hhtbase scheme databas heart sound use studi averag classif accuraci achiev two featur extract scheme generic linear discrimin analysi lda classifi use classif stage

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