Electromyographic signal dynamic behavior in neuropathies spectral parameters evaluation and classification

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods, such as Coherence, Phase Locking Factor (PLF), Fractal Dimension (FD), Lempel-Ziv (LZ) techniques, Detrended Fluctuation Analysis (DFA) and Multiscale Entropy (MSE) are used to analyze the surface electromiographic signal's chaotic behavior and evaluate different muscle groups' synchronization. Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction recorded from patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient and the control groups, whereas coherence, PLF and FD algorithms present results very similar for both groups. LZ, DFA and MSE algorithms appear then to be a good measure of corticospinal pathways integrity. A classification algorithm was applied to the results in combination with extracted features from the surface electromiographic signal, with an accuracy percentage higher than 70% for 118 combinations for at least one classifier. The classification results demonstrate capability to distinguish both groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method.