Noninvasive detection of mechanical prosthetic heart valve disorder

Authors:
Di Zhang;Jiazhong He;Jianping Yao;Yuequan Wu;Minghui Du
Affiliations:
School of Information Engineering, Guangdong Medical College, Dongguan, China and School of Electronics and Information, South China University of Technology, Guangzhou, China;Department of Physics, Shaoguan University, Shaoguan, China;Department of Cardiac Surgery of the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China;School of Electronics and Information, South China University of Technology, Guangzhou, China;School of Electronics and Information, South China University of Technology, Guangzhou, China
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

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.