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Intelligent Identification of Childhood Musical Murmurs
Journal of Healthcare Engineering
  • Yuerong Chen, University of Akron, main campus
  • Shengyong Wang, University of Akron Main Campus
  • Chia-Hsuan Shen, University of Akron, main campus
  • Fred K. Choy, University of Akron Main Campus
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Heart murmurs are often the first signs of heart valvular disorders. However, most heart murmurs detected in children are innocent musical murmurs (also called Still's murmurs), which should be distinguished from other murmur types that are mostly pathological, such as regurgitant, obstructive, and flow murmurs. In order to reduce both unnecessary healthcare expenditures and parental anxiety, this study aims to develop algorithms for intelligently identifying musical murmurs in children. Discrete wavelet transform was applied to phonocardiographic signals to extract features. Singular value decomposition was applied on the matrix derived from continuous wavelet transform to extract extra features. The sequential forward feature selection algorithm was then utilized to select significant features. Musical murmurs were subsequently differentiated via a classification procedure consisting of three classification techniques: discriminant analysis, support vector machine, and artificial neural network. The results of 89.02% sensitivity, 84.76% specificity and 87.36% classification accuracy were achieved.

Citation Information
Yuerong Chen, Shengyong Wang, Chia-Hsuan Shen and Fred K. Choy. "Intelligent Identification of Childhood Musical Murmurs" Journal of Healthcare Engineering Vol. 3 Iss. 1 (2012) p. 125 - 139
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