Authors:
Hichem Bengacemi
1
;
2
;
Abdenour Hacine Gharbi
3
;
Philippe Ravier
2
;
Karim Abed-Meraim
2
and
Olivier Buttelli
2
Affiliations:
1
Signal Processing Lab, École Militaire Polytechnique, Algiers, Algeria
;
2
PRISME Lab, Université d’Orléans, INSA-CVL, 12 Rue de Blois, 45067, Orléans, France
;
3
LMSE laboratory, University Mohamed El Bachir El Ibrahimi of Bordj Bou Arreridj, El-Anasser, Bordj Bou Arréridj, 34030, Algeria
Keyword(s):
Parkinson’s Disease Diagnostic, sEMG Signal Classification, sEMG Signal Segmentation, Wavelet Cepstral Coefficient (WCC), HMM Models.
Abstract:
To increase the diagnostic accuracy, the techniques of artificial intelligence can be used as a medical support. The Electromyography (EMG) signals are used in the neuromuscular dysfunction evaluation. This paper proposes a new frame work for segmenting and classifying the surface EMG (sEMG)signals by segmenting the EMG signal in regions of muscle activity (ACN) and non activity (NAN) for control group (healthy) and the muscle activity (ACP) and non activity (NAP) for Parkinsonian group. This paper proposes an automatic system of the neuromuscular dysfunction identification for Parkinson disease diagnosis based on HMM modeling by using on sEMG signals. Discrete Wavelet Transform (DWT), LP coefficients and FLP coefficients have been used for feature extraction. The results have been evaluated on ECOTECH project database using the signal classification rate ( CRS) and the Accuracy (Acc) criterion. The obtained results show highest performance by using HMM models of 2 states associated
with GMM of 6 Gaussians, combined with Log Wavelet decomposition based Energy(LWE) descriptor based on Coiflet wavelet mother with decomposition level of 4. The proposed methodology leads to a classification accuracy of leads to an Acc of 99.37 % and a CRS of 100 %.
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