Surface EMG Signal Segmentation and Classification for Parkinson’s Disease Based on HMM Modelling

Hichem Bengacemi, Hichem Bengacemi, Abdenour Hacine Gharbi, Philippe Ravier, Karim Abed-Meraim, Olivier Buttelli

2024

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 %.

Paper Citation

in Harvard Style

Bengacemi H., Hacine Gharbi A., Ravier P., Abed-Meraim K. and Buttelli O. (2024). Surface EMG Signal Segmentation and Classification for Parkinson’s Disease Based on HMM Modelling. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 930-938. DOI: 10.5220/0012572900003654

in Bibtex Style

@conference{icpram24,
author={Hichem Bengacemi and Abdenour Hacine Gharbi and Philippe Ravier and Karim Abed-Meraim and Olivier Buttelli},
title={Surface EMG Signal Segmentation and Classification for Parkinson’s Disease Based on HMM Modelling},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={930-938},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012572900003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Surface EMG Signal Segmentation and Classification for Parkinson’s Disease Based on HMM Modelling
SN - 978-989-758-684-2
AU - Bengacemi H.
AU - Hacine Gharbi A.
AU - Ravier P.
AU - Abed-Meraim K.
AU - Buttelli O.
PY - 2024
SP - 930
EP - 938
DO - 10.5220/0012572900003654
PB - SciTePress