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Using Kullback-Leibler Distance in Determining the Classes for the Heart Sound Signal Classification

  • Yong-Joo Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

Many research efforts have been done on the automatic classification of heart sound signals to support clinicians in heart sound diagnosis. Recently, hidden Markov models (HMMs) have been used quite successfully in the automatic classification of the heart sound signal. However, in the classification using HMMs, there are so many heart sound signal types that it is not reasonable to assign a new class to each of them. In this paper, rather than constructing an HMM for each signal type, we propose to build an HMM for a set of acoustically-similar signal types. To define the classes, we use the KL (Kullback-Leibler) distance between different signal types to determine if they should belong to the same class. From the classification experiments on the heart sound data consisting of 25 different types of signals, the proposed method proved to be quite efficient in determining the optimal set of classes.

Keywords

Classification Accuracy Hide Markov Model Signal Type Heart Sound Mitral Valve Prolapse 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yong-Joo Chung
    • 1
  1. 1.Department of ElectronicsKeimyung UniversityDaeguSouth Korea

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