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Related Experiment Videos

Phonocardiographic signal analysis method using a modified hidden Markov model.

Ping Wang1, Chu Sing Lim, Sunita Chauhan

  • 1Biomedical Engineering Research Centre, Nanyang Technological University, 50 Nanyang Drive, Research Techno Plaza, 6th Storey, XFrontiers Block, Singapore 637553, Singapore.

Annals of Biomedical Engineering
|December 16, 2006
PubMed
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This study introduces a novel system using Mel-frequency cepstral coefficients (MFCC) and hidden Markov models (HMM) for accurate heart sound classification. The developed method significantly improves diagnostic interpretation for cardiovascular analysis.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Auscultation is a key diagnostic tool for cardiovascular assessment.
  • Accurate heart sound classification is crucial for auscultative diagnosis.
  • Existing methods require efficient feature extraction and classification techniques.

Purpose of the Study:

  • To develop and evaluate a system for interpreting heart sounds using pattern recognition.
  • To compare the efficacy of different feature extraction methods for heart sound analysis.
  • To assess the performance of Mel-frequency cepstral coefficients (MFCC) combined with hidden Markov models (HMM) for heart sound classification.

Main Methods:

  • Heart sound cycles were pre-processed for feature extraction.

Related Experiment Videos

  • Feature extraction was performed using time-domain features, short-time Fourier transforms (STFT), and MFCC.
  • A hidden Markov model (HMM) was employed for automatic classification of heart sounds.
  • The system was evaluated on 1398 datasets from 41 subjects.

    • Main Results:

    • Mel-frequency cepstral coefficients (MFCC) demonstrated superior performance in feature extraction compared to other methods.
    • The MFCC-HMM system achieved high classification accuracy: sensitivity >= 0.952 and specificity >= 0.953.
    • The system successfully classified normal heart sounds and various murmur characteristics.
    • Constituent characteristics of heart sounds were effectively evaluated.

    Conclusions:

    • The proposed MFCC-HMM system offers improved interpretative information for heart sound analysis.
    • The system shows potential as an objective tool to aid clinicians in cardiovascular diagnosis.
    • This approach enhances the diagnostic capabilities of phonocardiography through advanced pattern recognition.