ANÁLISIS DE PROXIMIDAD DE MODELOS OCULTOS DE MARKOV PARA LA IDENTIFICACIÓN DE FUENTES DE ESPIGAS
Abstract
Recientemente, los modelos ocultos de Markov (HMM) se han usado para identificar fuentes de espigas en el tratamiento de la enfermedad de Parkinson. El criterio de clasificación que suele emplearse es la regla MAP (máximo a posteriori) para reconocer la clase correcta. Sin embargo, la clasificación puede mejorarse usando análisis de proximidad transformando las matrices que caracterizan el proceso de Markov a un espacio en el que se ven mejor reflejadas las similaridades y las diferencias entre los parámetros de estas matrices. Se presenta la aplicación del enfoque de análisis de proximidad usando HMM en la identificación de fuentes de espigas (Tálamo y Subtálamo, GPi y GPe). Los resultados muestran que el análisis de proximidad entrega mejores resultados (4% en promedio) que el enfoque tradicional.Downloads
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