Classification and signal processing analysis of the pathological electromyogram signal
by A. Mokdad; S.M. Debbal; F. Meziani
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 14, No. 3, 2022

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.

Online publication date: Tue, 19-Apr-2022

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