EMG Signal Analysis for Diagnosis of Muscular Dystrophy Using Wavelet Transform, SVM and ANN

Abstract

Implementation of Artificial intelligence techniques is used as a medical diagnostic tool to increase the diagnostic accuracy and provide more additional knowledge. Muscular dystrophy is a disorder which diagnosed with Electromyography (EMG) signals. A Wavelet-based decomposition technique is proposed here to classified Healthy EMG signals (Normal) from abnormal muscular dystrophy EMG signals. In this work, a wavelet transform is applied to preprocessed EMG signals for decomposing it into different frequency sub-bands. Statistical analysis is carried out to these decomposed sub-bands to extract different statistical features. SVM and ANN classifier is proposed here to discriminate muscular dystrophy disorder from healthy Electromyography signals. Finally proposed methodology gives classification accuracy of 95% on publically available clinical EMG database. The results show better classification accuracy using an SVM classifier compare to ANN classifier on selected statically feature sets. The finding from the above method gave the best classifier for analysis and classification of EMG signals for recognition of muscular dystrophy disorders.