Detector de Neuropatologías en EEG usando Estadísticas de Orden Superior y Aprendizaje Profundo
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https://doi.org/10.54139/revinguc.v28i1.14Palabras clave:
EEG, estadísticas de orden superior, aprendizaje profundo, red neuronal convolucional pre-entrenada Inception.Resumen
En el presente artículo se presenta un detector de neuropatologías, a partir del electroencefalograma (EEG) del paciente. La detección se basa en la clasificación de imágenes de HOSA (siglas en inglés para análisis de estadísticas de orden superior o “High Order Statistical Analysis”) derivadas de series de tiempo correspondientes a EEG de pacientes humanos. El clasificador es un modelo de aprendizaje profundo DL (“Deep Learning”) con la arquitectura de la CNN (Red Neuronal Convolucional o “Convolutional Neural Networks”) pre-entrenada: “Inception”. El conjunto de entrenamiento y prueba de la CNN son imágenes de HOSA, que representan los cumulantes de tercer orden de segmentos no lineales y no gaussianos, de señales correspondientes al canal seleccionado del EEG de pacientes con neuropatologías (específicamente, epilepsia) o sanos. El desempeño del clasificador es muy satisfactorio, presentando una exactitud de aproximadamente 94 % en la detección de epilepsia.
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Derechos de autor 2023 Revista Ingeniería UC
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
