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.

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

“…Some authors consider that there is noise also in higher frequencies, and thus they use filters that cancel up to 20 Hz [2,6,8,13,[15][16][17]19,21,23,24]. Nevertheless, in [7], the authors suppressed frequencies between 90 and 250 Hz and, in [25], the authors removed frequencies lower than 5 Hz and higher than 375 Hz. Table 1 summarizes the band-pass frequency allowed by each study.…”

“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

“…EMG signals also allow the diagnosis of muscular dystrophy disorder. Kehri and Awale [25] compared ANN with SVM in the classification of EMG signals to identify this disorder by using a wavelet-based decomposition technique. The results show the classification accuracy of an available clinical EMG database, of 140 samples, with 95% effectiveness using a polynomial kernel of fifth order in an SVM.…”

A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM

“…There are various techniques with numerous complexity in time and frequency domain, which shows different FEMG characteristics [5]. For feature extraction, we implemented a WT method that generates wavelet coefficients [29][30][31][32][33][34][35][36]. An active part of FEMG data containing 8000 samples with a sampling frequency of 1000Hz.…”

A Control System of SEMG Signal Based on Deep Learning

“…Literature [1,2] studies the acquisition and preprocessing of signals, literature [3,4] studies the recognition and classification of signals, and has achieved some results, but there are still some problems in the above literature, including the accuracy of continuous multi actions recognition, and the content of the research is discrete, so this paper proposes the integration of the current popular deep learning technology, designs and realizes a complete set The whole EMG signal control system. In this paper, only convolutional neural network method is used to classify gestures [5][6][7], and the classification results are matched to the motors which control the degree of freedom of the manipulator. The introduction of convolutional neural network reduces the time and labor cost of signal feature extraction [8][9][10][11].…”