Authors:
Rima Touahria
1
;
2
;
Abdenour Hacine-Gharbi
2
and
Philippe Ravier
3
Affiliations:
1
ETA Laboratory, University of Bordj Bou Arréridj, Elanasser 34030 Bordj Bou Arréridj, Algeria
;
2
LMSE Laboratory, University of Bordj Bou Arréridj, Elanasser, 34030 Bordj Bou Arréridj, Algeria
;
3
PRISME Laboratory, University of Orléans - INSA CVL, 12 rue de Blois, 45067 Orléans, France
Keyword(s):
PCG Signal, Features Extraction, Discrete Wavelet Transform, Wavelet Cepstral Coefficients, MFCC Coefficients, Hidden Markov Model, Classification.
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 H
MM 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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