Diagnosis of Neuromuscular Disorders Using Spectrogram Textural Features of EMG Signals

Authors

Department of Computer Engineering, Faculty of Engineering, Razi University, Kermanshah, Iran

Abstract

Neuromuscular disease includes many disorders that damage functioning of muscles or nerves. For automatic diagnosis of these abnormalities, surface EMG signals are processed using computer programs. Feature extraction and classification are two fundamental steps in processing EMG signals by the programs. This paper presents a novel approach based on textural analysis of time-frequency image of EMG signals. In contrast to previous time alone, frequency alone, and time-frequency wavelet-based approaches, exploiting relational time and frequency properties of spectral image of EMG signal, high discrimination is achieved in the form of a compact feature vector by the proposed method. To assign each feature to it ҆s respective class, various classification methods including: KNN, SVM, LDA, logic regression, and their combination are examined in two segmented and holistic modes in the present study. At last, EMG dataset in lower limb is utilized to evaluate the proposed method. Using SVM with RBF kernel to classify segmented features, the classification accuracy of 89.4% obtained by the proposed method. The obtained classification accuracy has been improved by 3.40% over the best previous result.

Keywords

References

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TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 48, Issue 4 - Serial Number 86
February 2018
Pages 1633-1644

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