Saied Piri - Research Center for Computational Cognitive Neuroscience, System & Cybernetic Laboratory, Imam Reza International University, Mashhad, Iran
Arefeh Dinarvand - UAST-University of Applied Science and Technology X-IBM Institute, Tehran, Iran
Kazem Sohrabi - Bachelor of Aerospace Engineering majoring in air structures, Shahid Sattari Aeronautical University, Tehran, Iran

in this paper, a deep learning framework for detection and classification of EMG signals for diagnosis of neuromuscular disorders is proposed employing cross wavelet transform. Cross wavelet transform which is a modification of continuous wavelet transform is an important tool to analyze any non-stationary signal in time scale and in time-frequency frame. To this end, EMG signals of healthy, myopathy and Amyotrophic lateral sclerosis disorders were procured from an online existing database. A healthy EMG signal was chosen as reference and cross wavelet transform of the rest of the healthy as well as the disease EMG signals was done with the reference. From the resulting cross wavelet spectrum images of EMG signals, a convolution neural network (CNN) based automated deep feature extraction technique was implemented. The extracted deep features were further subjected to feature ranking employing one way analysis of variance (ANOVA) test. The extracted deep features with high degree of statistical significance were fed to several benchmark machine learning classifiers for the purpose of discrimination of EMG signals. Two binary classification problems are addressed in this paper and it has been observed that the highest mean classification accuracy of ۱۰۰% is achieved using the statistically significant extracted deep features. The proposed method can be implemented for real-time detection of neuromuscular disorders.

Convolution neural network, classification, cross-wavelet transform and electromyography