Authors:
Maria Marta Santos
1
;
Ana Luisa Gomes
1
;
Hugo Gamboa
1
;
Mamede Carvalho
2
;
Susana Pinto
2
and
Carla Quintão
3
Affiliations:
1
Universidade Nova de Lisboa and PLUX - Wireless Biosignals, Portugal
;
2
Universidade de Lisboa, Portugal
;
3
Universidade Nova de Lisboa and Universidade de Lisboa, Portugal
Keyword(s):
Amyotrophic Lateral Sclerosis (ALS), Coherence, Phase Locking Factor (PLF), Fractal Dimension (FD), Lempel-Ziv (LZ), Detrended Fluctuation Analysis (DFA), Multiscale Entropy (MSE) , Surface Electromyography (sEMG), Ipsilateral, Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Detection and Identification
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Time and Frequency Response
;
Time-Frequency Analysis
Abstract:
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 algo
rithm 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.
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