Diagnosis of Neuromuscular Disorders Using Machine Learning Techniques

Abstract

The neuromuscular disorders are identified by using electromyographic (EMG) signals. Machine learning techniques are used as a decision support system to diagnose the neuromuscular disorders. The time-frequency methods are widely employed to extract features from the EMG signals for the diagnosis of the neuromuscular disorders. In this chapter, wavelet-based time-frequency techniques are compared for the automatic classification of EMG signals. The experiments are carried out to categorize EMG signals into ALS (amyotrophic lateral sclerosis), control, or myopathic. The proposed framework is composed of three main modules. In the first module, EMG signals are denoised by using MSPCA (multiscale principal component analysis) denoising technique. In the second module, the coefficients of wavelet-based time-frequency methods are calculated for each category of EMG signal, and then statistical values of each sub-band are computed. In the last module, the extracted features are employed as an input to a classifier to diagnose different neuromuscular disorders. The obtained results obviously show that features extracted by using DT-CWT (dual-tree complex wavelet transform) are highly discriminative for the MUAP (motor unit action potential) classification as compared to other wavelet-based time-frequency methods. Using DT-CWT features along with the SVM (support vector machine) classifier accomplished a classification accuracy of 99.6%. Hence, the proposed technique can be employed for the diagnosis of neuromuscular disorders as a decision support system.