A novel machine learning approach based on fast multi-scale hybrid wavelet network for supporting diagnosis of neuromuscular disorders

Abstract

Electromyography (EMG) is the most used clinical and biological signal, which is an essential element for detecting several neurodegenerative and neuromuscular disorders such as ALS, myopathy, neuropathy etc. Among various functional abnormalities of the motor neuron, Amyotrophic Lateral Sclerosis (ALS) is one of the deadliest neurodegenerative diseases. Several studies have been conducted recently; researchers struggle to find an appropriate approach to enhance the identification mechanism for ALS disease. In this paper, we developed a new algorithm based on Multi-resolution analysis, Fast Wavelet Transform and wavelet network served as feature extraction, selection, and reduction all in one technique. The results of our study are adequate for the classification of ALS, Normal and Myopathy patients. Furthermore, our approach with the AdaBoost classifier outperformed all the recent studies with 100% overall accuracy.