Análisis de señales acústicas de pulmón basado en coeficientes cepstrales de la escala Mel y mapas auto-organizados
Resumen
En este trabajo se realizó un análisis de anormalidades en señales acústicas de pulmón. La metodología incluyó el uso de coeficientes cepstrales de la escala Mel (MFCC), Mapas Auto-Organizados (SOM) y el algoritmo de agrupamiento K-means. Los modelos obtenidos con los mapas son conocidos como redes neuronales artificiales, que pueden ser entrenados en una forma supervisada o no supervisada. Ambos tipos de entrenamiento fueron usados para comparar el uso de este tipo de herramientas computacionales en estudios de señales respiratorias. Los resultados mostraron un 85 % de acierto en la clasificación, cuando fue implementado un entrenamiento supervisado. Al realizar tareas de agrupamiento con entrenamiento no supervisado fue encontrado que el número de grupos más adecuado es de tres. En general, los modelos SOM pueden ser usados en este tipo de señales como una estrategia útil en sistemas de diagnóstico, encontrando información en los datos y realizando clasificación para sistemas de apoyo a decisión.Palabras clave
mapas auto-organizados, señales acústicas de pulmón, sistemas de apoyo a decisión
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