Detection of heart murmurs using wavelet analysis and artificial neural networks

Nicholas Andrisevic, Khaled Ejaz, Fernando Rios-Gutierrez, Rocio Alba-Flores, Glenn Nordehn, Stanley G Burns

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Original languageEnglish (US)
Pages (from-to)899-904
Number of pages6
JournalJournal of biomechanical engineering
Volume127
Issue number6
DOIs
StatePublished - Nov 1 2005

Keywords

  • Artificial Neural Networks
  • Cardiovascular
  • Digital Image Processing
  • Heart Murmur
  • Medical Diagnostic Device
  • Wavelet Packet Analysis

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