Detecção de crises epilépticas a partir de sinais eletroencefalográficos

• Autor(a) principal: Parreira, Fábio José
• Data de Publicação: 2006
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/14290
• topic: Eletroencefalograma; Epilepsia; Wavelet; FFT; Modelo auto-regressivo; Redes neurais; Detecção; Engenharia biomédica; Eletroencefalografia; Electroencephalogram; Epilepsy; Wavelet transform; Autoregressive model (AR); Neural networks; Detection; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

The epilepsy is not a recent phenomenon, even its has being approached and Inves- tigated, this area still demands several researches and it is far away from being totally explained. The obtaining of the primordial features to di®erentiate the epileptic events of the others, in coming signs EEG of scalp, it represents a great challenge, since exist to many artifacts, and these are confused with epileptic events. In this sense, this study presents the development of architectures destined to detect events of epilepsy in coming signs EEG of scalp, capable to aid the professionals of the health in the study of this pathology To accomplish the objectives, ¯rstly was developed an application capable to visualize EEG and to segment the electroencephalogram plan to form the base of data Concerning to the detection of the pathological signs, four architectures were proposed. The architecture with analysis multi-resolution used the \ wavelet " (WT) for extraction of features, as well as neural networks and specialist system for recognition. For that architecture the best gotten results obtained a rate of 71,6 % of success, with 28,3 % of error. The sensibility was around 83,3 %, the speci¯city 70,5 % and the precision 76,9 %. The statistical architecture is directly composed of tools for features extraction of the sign. The best success rate was around 85,3 %, the obtained error was of 14,3 % and the inde¯nite ones around 1 %. The sensibility was of 97,4 %, the speci¯city 82,1 % and the precision 89,75 %. The architecture of analysis multi-resolution and AR possesses two stages for extraction of feature: the \ wavelet ", following by the AR models. For that architecture they used two AR models . The best success rate for the \ Yule-Walker"model was around 87,9 %, with order 10. Already in the results of the \ Burg"model, the best success rate was of 88,5 % with order 7. For the last architecture is a hybrid model with several tools of extraction of features in the domain of the time, frequency (FFT) and time-frequency (WT). In that architecture the success rate was in 95,1 %, the error 4,1 % the inde¯nite ones 5,5 %. The speci¯city was of 91,5 %, the obtained sensibility was of 90,5 % and the precision around 91,1 %. Therefore all of the developed systems presented quite coherent results among the phenomena demarcated by the professionals of the medical area and those revealed by the architectures, mainly for the case of the hybrid architecture that presented the best rates.