7 resultados para Dimensión fractal
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Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Física, especialidade de Engenharia de Superfícies, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
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Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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IEE Proceedings - Vision, Image, and Signal Processing, Vol. 147, nº 1
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IEE Proceedings - Vision, Image, and Signal Processing, Vol. 147, nº 1
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Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Física - Física Aplicada pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
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Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Geografia e Planeamento Regional, Especialidade em Novas Tecnologias em Geografia
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Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by motor neurons degeneration, which reduces muscular force, being very difficult to diagnose. Mathematical methods are used in order to analyze the surface electromiographic signal’s dynamic behavior (Fractal Dimension (FD) and Multiscale Entropy (MSE)), evaluate different muscle group’s synchronization (Coherence and Phase Locking Factor (PLF)) and to evaluate the signal’s complexity (Lempel-Ziv (LZ) techniques and Detrended Fluctuation Analysis (DFA)). Surface electromiographic signal acquisitions were performed in upper limb muscles, being the analysis executed for instants of contraction for ipsilateral acquisitions for patients and control groups. Results from LZ, DFA and MSE analysis present capability to distinguish between the patient group and the control group, 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 members between patients and control groups. These results can demonstrate a major importance in the disease diagnose, once surface electromyography (sEMG) may be used as an auxiliary diagnose method.