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.
Highlights
The Electromyogram (EMG) is a biomedical signal defined by an electrical potential, produced by muscles cells
EMG signals taken from the publicly available database are divided into two groups
Artificial Neural Network (ANN) Results In this approach, the ANN classifier is implemented for the classification of EMG datasets
Summary
The Electromyogram (EMG) is a biomedical signal defined by an electrical potential, produced by muscles cells. Qualitative EMG analysis cannot give data for comparison and classifying EMG disorders To overcome this problem, computer-based EMG algorithms have been proposed[3]. The literature has shown a various combination of classification technique with extracted features sets for diagnosis of muscular disorders. Pattichis et al in[9] used a wavelet transform (WT) for extracting EMG features and applied different neural networks for EMG classification. In14 a combined technique of parametric power spectral and features extraction through WT is used for EMG signal analysis using neuro-fuzzy classifier. Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers are proposed here Both the classifier having great predictive power effectively implemented for medical diagnostic decision support system. Several authors have shown their research interest in integrating the prediction of several systems often results in a classification accuracy that is higher than that of individual systems
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