Selection of wavelet packet measures for insufficiency murmur identification

Authors:
Samjin Choi;Youngkyun Shin;Hun-Kuk Park
Affiliations:
Department of Biomedical Engineering & Healthcare Industry Research Institute, College of Medicine, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea;Department of Electrical Engineering, Yuhan University, 636 Gyeongin-ro, Sosa-gu, Bucheon-si, Gyeonggi-do 422-749, Republic of Korea;Department of Biomedical Engineering & Healthcare Industry Research Institute, College of Medicine, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea and Program ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper presents a new analysis method for aortic and mitral insufficiency murmurs using wavelet packet (WP) decomposition. We proposed four diagnostic features including the maximum peak frequency, the position index of the WP coefficient corresponding to the maximum peak frequency, and the ratios of the wavelet energy and entropy information to achieve greater accuracy for detection of heart murmurs. The proposed WP-based insufficiency murmur analysis method was validated by some case studies. We employed a thresholding scheme to discriminate between insufficiency murmurs and control sounds. Three hundred and thirty-two heart sounds with 126 control and 206 murmur cases were acquired from four healthy volunteers and 47 patients who suffered from heart defects. Control sounds were recorded by applying a wireless electric stethoscope system to subjects with no history of other heart complications. Insufficiency murmurs were grouped into two valvular heart defect categories, aortic and mitral. These murmur subjects had no other coexistent valvular defects. The proposed insufficiency murmur detection method yielded a high classification efficiency of 99.78% specificity and 99.43% sensitivity.