Estudo de sinais de matrizes multieletrodo (MEAs) em termos do janelamento

• Ano de defesa: 2010
• Autor(a) principal: Cardoso, Rodrigo Ribeiro
• Orientador(a): Destro Filho, João Batista
• Banca de defesa: Nomura, Shigueo , Pereira, Andrea Antunes , Saito, José Hiroki , Zanon, Renata Graciele
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia
• Programa de Pós-Graduação: Programa de Pós-graduação em Engenharia Elétrica
• Departamento: Engenharias
• País: BR
• Palavras-chave em Português: Matriz multieletrodo; MEA; Eletroencefalografia; EEG; Segmentação; SEM; Autocorrelação; Engenharia biomédica; Processamento de sinais; Eletrocencefalografia
• Palavras-chave em Inglês: Multielectrode array; Electroencephalography; Segmentation; Autocorrelation
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14436

This work studies a segmentation tool for application to multielectrode arrays (MEAs) signals. Studies of neuronal cultures signals by means of MEA have opened a wide research field. However, classical tools for analyzing these kinds of signals have focused on spikes detection and on time series intervals between spikes (ISI). The search for new tools on signal analysis and segmentation can boost investigations and their applications. There is opportunity for classical mathematical tools such as autocorrelation yet. Results and discussions on application of this technique in multielectrode array signals are presented. The application of this tool in cellular inactivity identification has great potential. An algorithm applying the segmentation technique by SEM (Spectral Error Measurement) is also presented. The results are not conclusive; however, new possibilities are opened for future investigation on this technique and others techniques of signals segmentation for applying on MEA signals. Finally, comparative applications of the developed tools in electroencephalographic signals (EEG) are presented.