Title: Classification and signal processing analysis of the pathological electromyogram signal

Authors: A. Mokdad; S.M. Debbal; F. Meziani

Addresses: Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B. Belkaid-Tlemcen, BP 119, Tlemcen, BP 119, Algeria ' Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B. Belkaid-Tlemcen, BP 119, Tlemcen, BP 119, Algeria ' Genie Biomedical Laboratory (GBM), Faculty of Technology, University A.B. Belkaid-Tlemcen, BP 119, Tlemcen, BP 119, Algeria

Abstract: This paper presents the classification of EMG signals for amyotrophic lateral sclerosis (ALS) disorder based on time-frequency method applying spectrogram. Four features [Shannon entropy (SSE), spectal entropy (SE), mean frequency (MNF) and median frequency (MDF)] are extracted from spectrogram in order to qualify the most appropriate one. So, two machine learning classifier of linear discriminate analysis (LDA) and support vector machine (SVM) are implemented to EMG signal. From the experiment, SVM classifier is outperformed others using SSE features from spectrogram which is more than 80% with an optimal window size of 512 ms. The finding of the study concludes that SVM is recommended to classify ALS disorder and can help rehabilitation centre to diagnose their patients in advanced state.

Keywords: amyotrophic lateral sclerosis; ALS; spectrogram; classify; support vector machine; SVM; linear discriminate analysis; LDA.

DOI: 10.1504/IJMEI.2022.122286

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.3, pp.272 - 281

Received: 06 Jan 2020
Accepted: 12 Aug 2020
Published online: 19 Apr 2022 *

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