• DocumentCode
    153004
  • Title

    Cardiac arrhythmia analysis using Hidden Markov Model and murmur diagnosis

  • Author

    Arslan, A. ; Yildiz, O.

  • Author_Institution
    Bilgisayar Muhendisligi Bolumu, Yildirim Beyazit Univ., Ankara, Turkey
  • fYear
    2014
  • fDate
    23-25 April 2014
  • Firstpage
    2031
  • Lastpage
    2034
  • Abstract
    The heart is the most important of the vital organs and heart diseases may cause fatal consequences. Abnormal heart sounds called murmur may be a precursor of many serious heart diseases. Cardiac auscultation is a basic technique to easily diagnose murmur disease. Auscultation can be supported by using computer aided automatic diagnosis systems to fast and accurate diagnosis. These systems are useful to remote diagnosis systems in place that have the lack of physicians and modern techniques. In this study, heart sound data taken from different patients and recorded with the help of an electronic stethoscope are classified by using Hidden Markov Model. At the end of this study, healthy heart and five different murmur diseases; can be detected with full success just listening heart sound by an automatic system.
  • Keywords
    computerised instrumentation; diseases; hidden Markov models; medical signal processing; phonocardiography; signal classification; abnormal heart sounds; cardiac arrhythmia analysis; cardiac auscultation; classification; computer aided automatic diagnosis systems; electronic stethoscope; fatal consequences; heart diseases; heart sound data; hidden Markov model; murmur disease diagnosis; remote diagnosis systems; vital organs; Computers; Diseases; Heart; Hidden Markov models; Markov processes; Mel frequency cepstral coefficient; Signal processing; classification; early diagnosis systems; heart sounds; hidden markov model; murmur diagnosis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2014 22nd
  • Conference_Location
    Trabzon
  • Type

    conf

  • DOI
    10.1109/SIU.2014.6830658
  • Filename
    6830658