Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models

Rima Touahria, Rima Touahria, Abdenour Hacine-Gharbi, Philippe Ravier

Abstract

This paper proposes the use of several features based on Discrete Wavelet Transform as novel descriptors for the application of classifying normal or abnormal phonocardiogram (PCG) signals, using Hidden Markov Models (HMM). The feature extraction of the first descriptor called “DWE” consists in converting each PCG signal into a sequence of features vectors. Each vector is composed of the energy of the wavelet coefficients computed at each decomposition level from an analysis window. The second descriptor “LWE” consists in applying the logarithm of DWE features, while the third descriptor “WCC” applies the DCT on the LWE features vector. This work aims to find the relevant descriptor using PCG Classification Rate criterion. This is achieved by implementing a standard system of classification using the HMM classifier combined with MFCC features descriptor. Each class is modeled by HMM model associated to GMM model. Several experiences are carried out to find the best configuration of HMM models and to select the optimal mother wavelet with its optimal decomposition level. The results obtained from a comparative study, have shown that the LWE descriptor using Daubechies wavelets at order 2 at level 7, gives the highest performance classification rate, with a more compact features representation than the MFCC descriptor.

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Paper Citation


in Harvard Style

Touahria R., Hacine-Gharbi A. and Ravier P. (2021). Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models.In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 334-340. DOI: 10.5220/0010343003340340


in Bibtex Style

@conference{icpram21,
author={Rima Touahria and Abdenour Hacine-Gharbi and Philippe Ravier},
title={Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2021},
pages={334-340},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010343003340340},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models
SN - 978-989-758-486-2
AU - Touahria R.
AU - Hacine-Gharbi A.
AU - Ravier P.
PY - 2021
SP - 334
EP - 340
DO - 10.5220/0010343003340340