Detección de Fibrilación Ventricular Mediante Tiempo-Frecuencia y Clasificador KNN sin Extracción de Parámetros

  1. Mjahad, Azeddine
  2. Rosado Muñoz, Alfredo
  3. Bataller Mompeán, Manuel
  4. Francés Víllora, Jose V.
  5. Guerrero Martínez, Juan F.
Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2018

Volumen: 15

Número: 1

Páginas: 124-132

Tipo: Artículo

DOI: 10.4995/RIAI.2017.8833 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Objetivos de desarrollo sostenible

Resumen

Este trabajo propone la detección de FV y su discriminación de TV y otros ritmos cardiacos basándose en la representación tiempo-frecuencia del ECG y su conversión en imágen como entrada a un clasificador de vecinos más cercanos (KNN) sin necesidad de extracción de parámetros adicionales. Tres variantes de datos de entrada al clasificador son evaluados. Los resultados clasifican la señal en cuatro clases diferentes: ’Normal’ para latidos con ritmo sinusal, ’FV’ para fibrilación ventricular, ’TV’ para taquicardia ventricular y ’Otros’ para el resto de ritmos. Los resultados para detección de FV mostraron 88,27% de sensibilidad y 98,22% de especificidad para la entrada de imágen equivalente reducida que es la más rápida computacionalmente a pesar de obtener resultados de clasificación ligeramente inferiores a las representaciones no reducidas. En el caso de TV, se alcanzó un 88,31% de sensibilidad y 98,80% de especificidad, un 98,14% de sensibilidad y 96,82% de especificidad para ritmo sinusal normal y 96,91% de sensibilidad con 99,06% de especificidad para la clase ’Otros’. Finalmente, se realiza una comparación con otros algoritmos.

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Fuente de los datos: Dialnet