Electromyographic Signal Dynamic Behavior in Neuropathies Spectral Parameters Evaluation and Classification Maria Marta Santos 1,2 , Ana Lu´ ısa Gomes 1,2 , Hugo Gamboa 1,2 , Mamede de Carvalho 3 , Susana Pinto 3 and Carla Quint˜ ao 1,4 1 Departamento de Fsica, Faculdade de Ciˆ encias e Tecnologia, Universidade Nova de Lisboa, Lisboa, Portugal 2 PLUX - Wireless Biosignals, Lisboa, Portugal 3 Faculdade de Medicina, Instituto de Medicina Molecular, Universidade de Lisboa, Lisboa, Portugal 4 Instituto de Biof´ ısica e Engenharia Biom´ edica, Faculdade de Ci´ encias, Universidade de Lisboa, Lisboa, Portugal Keywords: Amyotrophic Lateral Sclerosis (ALS), Coherence, Phase Locking Factor (PLF), Fractal Dimension (FD), Lempel-Ziv (LZ), Detrended Fluctuation Analysis (DFA), Multiscale Entropy (MSE) , Surface Electromyog- raphy (sEMG), Ipsilateral, Classification. Abstract: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degen- eration, 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 contrac- tion 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 distin- guish 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. 1 INTRODUCTION ALS is a fatal and very progressive disease, character- ized by both upper and lower motor neurons degen- eration, involving brainstem and also multiple spinal cord innervation regions. This disorder is responsi- ble for abnormal motor activity. ALS patients typ- ically present fatigue, quickly progressive weakness and reduced exercise capacity with loss of voluntary movement, spasticity, fasciculations, dysphagia (dif- ficulty in swallowing), dyspnea (difficulties in breath- ing) and dysarthria (difficulties in speaking). After the first symptoms, death may occur within 3 − 5 years for most of the patients. ALS is very difficult to diagnose, since there isn’t available a reliable biomarker of dis- ease activity and progression (Kiernan et al., 2011; Mitchell and Borasio, 2007). Upper motor neuron integrity can be evaluated through the investigation of oscillatory activity prop- agation. The motor cortex activity can be recorded, and both alpha (8 − 12Hz) and beta (15 − 30Hz) fre- quency bands can be analyzed via coherence and PLF (Farmer et al., 2007). This work explores the analysis of ipsilateral ac- quisitions, which was presented with promissory pre- liminary results in (Camara, 2013), using different ap- proaches. Motor unit recruitment patterns complexity can be quantified using FD. However, the strength of a mus- cle’s contraction is better estimated based on Max- imum Fractal Length (MFL), even for very small muscle contraction strength, rather than FD (Poos- apadi Arjunan and Kumar, 2012). The LZ measure is a well suited feature regard- 227 Marta Santos M., Luisa Gomes A., Gamboa H., Carvalho M., Pinto S. and Quintão C.. Electromyographic Signal Dynamic Behavior in Neuropathies - Spectral Parameters Evaluation and Classification. DOI: 10.5220/0005215602270234 In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 227-234 ISBN: 978-989-758-069-7 Copyright c  2015 SCITEPRESS (Science and Technology Publications, Lda.)