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Instituto Nacional de Neurología y Neurocirugía

2017, Número 3

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Arch Neurocien 2017; 22 (3)


Detección de cambios abruptos en señales epilépticas del EEG

Villazana LSA, Eblen ZAA, Montilla LGR, Seijas FCO

Idioma: Español
Referencias bibliográficas: 50
Paginas: 6-18
Archivo PDF: 630.08 Kb.


RESUMEN

La epilepsia es un trastorno crónico del cerebro que afecta a aproximadamente 60 millones de personas en todo el mundo. En promedio 30% de las personas con epilepsia, no responden a tratamiento con uno o más medicamentos ni a cirugía resectiva. Es bien sabido que la ocurrencia de las convulsiones epilépticas produce una serie de cambios dinámicos súbitos y repentinos en las señales cerebrales que se manifiestan como crisis parciales o generalizadas en el paciente epiléptico. En la presente investigación se desarrolló un índice de disimilitud para la detección de las convulsiones epilépticas en señales EEG basado en el modelo de detección de cambios abruptos, teniendo como soporte al clasificador de una sola clase obtenido con la máquina de aprendizaje conocida como descripción de datos basados en vectores de soporte (SVDD, del inglés Support Vector Data Description). Los rasgos basados en el diagrama de Poincaré que incluye la medida de correlación compleja, obtenidos a partir de las señales EEG fueron las entradas de los clasificadores de una clase para obtener el mencionado índice. El índice de disimilitud (ID) mostró que en el instante cuando se inicia la crisis epiléptica su valor fue mayor, manteniéndose elevado por algunas épocas. Se evidenció con claridad que el ID reveló un cambio en la distribución estadística de los conjuntos antes y después del instante de inicio de la convulsión. Se demostró que el ID basado en la SVDD para detección de la crisis epiléptica es un buen parámetro para caracterizar por medio electroencefalógrama una convulsión.


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