Uma abordagem baseada em redes neurais artificiais sobre o espectro de potência de eletroencefalogramas para o auxílio médico na classificação de crises epiléticas
Resumo
A epilepsia é a quarta enfermidade neurológica mais comum e atinge aproximadamente 1% da população mundial. O diagnóstico é, em geral, amparado por um eletroencefalograma (EEG), cuja análise depende da interpretação médica, o que por vezes gera incongruência de diagnóstico, além de ser um trabalho tedioso, impreciso e propenso a erros. Este trabalho propõe um método de reconhecimento automático de padrões baseado em aprendizado de máquina e engenharia de características aplicadas ao espectros de potência de segmentos de EEGs. Resultados sugerem a possibilidade de detectar crises epilépticas com uma precisão superior a 80% em bases de dados já utilizadas na literatura.
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