A Support Vector Machine (SVM) classification approach to heart murmur detection

Samuel Rud, Jiann-Shiou Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations


This paper focuses on the study of detecting low frequency vibrations from the human chest and correlate them to cardiac conditions using new devices and techniques, custom software, and the Support Vector Machine (SVM) classification technique. Several new devices and techniques of detecting a human heart murmur have been developed through the extraction of vibrations primarily in the range of 10 - 150 Hertz (Hz) on the human chest. The devices and techniques have been tested on different types of simulators and through clinical trials. Signals were collected using a Kardiac Infrasound Device (KID) and accelerometers integrated with a custom MATLAB software interface and a data acquisition system. Using the interface, the data was analyzed and classified by an SVM approach. Results show that the SVM was able to classify signals under different testing environments. For clinical trials, the SVM distinguished between normal and abnormal cardiac conditions and between pathological and non-pathological cardiac conditions. Finally, using the various devices, a correlation between heart murmurs and normal hearts was observed from human chest vibrations.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks - ISNN 2010 - 7th International Symposium on Neural Networks, ISNN 2010, Proceedings
Number of pages8
EditionPART 2
StatePublished - Jul 14 2010
Event7th International Symposium on Neural Networks, ISNN 2010 - Shanghai, China
Duration: Jun 6 2010Jun 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6064 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Symposium on Neural Networks, ISNN 2010


  • Hear murmur detection
  • support vector machine


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