Heart murmur detection with SVM classification

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

Abstract

This paper presents an approach to detect low frequency vibrations from the human chest and correlate them to cardiac conditions. Our system which includes data acquisition via a TekScan FlexiForce sensor, signal processing, and hardware/software interfacing is developed and tested through clinical trials. A Support Vector Machine (SVM) learning algorithm is used to train and classify signals. Our results show that a SVM is able to separate and distinguish signals between normal and abnormal cardiac conditions.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages228-229
Number of pages2
ISBN (Electronic)9781479987443
DOIs
StatePublished - Aug 20 2015
Event2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 - Taipei, Taiwan, Province of China
Duration: Jun 6 2015Jun 8 2015

Publication series

Name2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015

Other

Other2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
CountryTaiwan, Province of China
CityTaipei
Period6/6/156/8/15

Keywords

  • Clinical trials
  • Data acquisition
  • Heart
  • Support vector machines
  • Testing
  • Vibrations

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  • Cite this

    Yang, J. S. (2015). Heart murmur detection with SVM classification. In 2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 (pp. 228-229). [7216869] (2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-TW.2015.7216869