Intelligent identification of childhood musical murmurs

Yuerong Chen, Shengyong Wang*, Chia Hsuan Shen, Fred K. Choy

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)125-139
Number of pages15
JournalJournal of Healthcare Engineering
Issue number1
StatePublished - 03 2012
Externally publishedYes


  • Ensemble classification
  • Murmur differentiation
  • Phonocardiographic signal
  • Singular value decomposition
  • Wavelet transform


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