Automated diagnosis of amyotrophic lateral sclerosis using electromyograms and firefly algorithm based neural networks with fractional position update.

Abstract

Amyotrophic Lateral Sclerosis (ALS) is a disorder of the neuromuscular system that causes the impairment of nerve cells from brain to spinal cord and to the voluntary muscles in every part of the human physiological system, which totally leads to paralysis. The examination of ALS using Electromyograms (EMG) is a challenging task which requires experts to investigate and diagnose. Hence, the development of an efficient and automated procedure is significant for the analysis of ALS signals. In this work, eighty time-frequency features were extricated from EMG signals transformed into time-frequency images. Further, fifteen highly substantial features were chosen using the firefly algorithm with fractional position update. Further, fractional firefly neural network is introduced and developed to examine the EMG signals. The performance metrics of the fractional firefly based neural network diagnostic system were analyzed with different fractional orders (α) and hidden neurons. Results demonstrated that the proposed technique is highly efficient and yields good statistical significance. Further, the accuracy of the fractional firefly neural network classifier with α = 0.5 and 15 hidden neurons is higher (93.3%) when compared to the accuracy of the classifier with different α values and hidden neurons. The proposed fractional order-based feature selection algorithm and classifier model are highly suitable for development of systems for evaluation of ALS and normal EMG signals, since the proficient discrimination of normal and ALS EMG signals is essential for the identification of neuromuscular disorders.