Decomposição de sinais eletromiográficos utilizando filtros casados

• Ano de defesa: 2013
• Autor(a) principal: Siqueira Junior, Ailton Luiz Dias
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Eletromiografia; Decomposição de sinais EMG; Filtros casados; Processamento de sinais biomédicos; Electromyography; EMG signal decomposition; Matched filters; Biomedical singnal processing; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14324; https://doi.org/10.14393/ufu.te.2013.61

The detection and classification of EMG motor unit action potentials (MUAP) is an important tool in the study of the neuromuscular system, allowing for a number os applications, such as the diagnoses of motor disorders. However, although there are several methods described in the literature to perform such actions, the majority relies on complex algorithms and specific instrumentation. Depending on the system, the computational cost or the detection mechanism, sometimes involving electrode arrays, may limit its use in clinical applications, biofeedback or embedded systems for controlling artificial prostheses. Another important issue is the detection and classification of firing MUAPs in signals with low signal to noise ratio (SNR). A method capable of operating with low SNR is paramount for applications, such as the use of electromyography in human machine interfaces (HMI), where the positioning and fixation of the electrodes may be performed by a non-trained user, and the signal can be contaminated by high levels of electromagnetic interference. As a solution for such problems, two complementary methods were proposed: the first (MD-FC) is based on the use of banks of matched filters for detection and classification MUAPs in EMG signals, whereas the second (MAD-FC) is proposed as an improvement from the first, aiming situations with a high probability of overlapping firing MUAPs. The proposed methods sought to achieve those goals without an excessive increase in computational cost, treating signals with variable noise levels and considering the overlapping of MUAPs. The results showed that the MD-FC system is able to accurately detect 96% of isolated MUAPs in signals with SNR of 10 dB and up to 10 active motor units. However, the performance is reduced in the presence of high levels of overlapping MUAPs, as expected. The second method (MAD-FC) was designed to improve the detection of overlapping MUAPs. The results showed that the MAD-FC is able to detect and classify firing MUAPs in signals with up 10 active motor units and SNR of 20 dB at rates of success higher than 79.80%, on average. When the SNR is decreased to 10dB the rates of success reach at least 75.19%, on average (even in this case with a high percentage of overlapping). In general, the MAD-FC showed rates of success around 20% better than the MD-FC method. Both methods are quite efficient when using computational resources. They were created in order to process EMG windows of 50 milliseconds in less than 5 milliseconds, when using a standard desktop computer. This feature allows their use in applications requiring MUAPs detection and classification in real time.