TY - CONF
T1 - Feature extraction and classification of heart murmurs using heart sound
AU - Chen, Yuerong
AU - Shen, Chia Hsuan
AU - Wang, Shengyong
AU - Choy, Fred K.
AU - Cutler, David
PY - 2010
Y1 - 2010
N2 - Heart murmurs are often the important representations of heart valve disorders. But not all murmurs are pathological. For example, musical murmurs are normal in children. In order to reduce healthcare expenditures and unnecessary parental anxieties, this study aims to develop techniques to distinguish musical murmurs from other murmurs most of which are organic. Wavelet transform and t-test are used for feature extraction and selection, respectively. An ensemble classification method is developed from three classification techniques: Discriminant analysis, support vector machine, and artificial neural network. The computational results demonstrate high sensitivity and specificity.
AB - Heart murmurs are often the important representations of heart valve disorders. But not all murmurs are pathological. For example, musical murmurs are normal in children. In order to reduce healthcare expenditures and unnecessary parental anxieties, this study aims to develop techniques to distinguish musical murmurs from other murmurs most of which are organic. Wavelet transform and t-test are used for feature extraction and selection, respectively. An ensemble classification method is developed from three classification techniques: Discriminant analysis, support vector machine, and artificial neural network. The computational results demonstrate high sensitivity and specificity.
KW - Ensemble classification
KW - Murmur classification
KW - Murmur feature extraction and selection
KW - Wavelet detail coefficients
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=84901009085&partnerID=8YFLogxK
M3 - 论文
AN - SCOPUS:84901009085
T2 - IIE Annual Conference and Expo 2010
Y2 - 5 June 2010 through 9 June 2010
ER -