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.
Journal:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Year of publication: 2018

Volume: 15

Issue: 1

Pages: 124-132

Type: Article

DOI: 10.4995/RIAI.2017.8833 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista iberoamericana de automática e informática industrial ( RIAI )

Abstract

This work describes new techniques to improve VF detection and its separation from Ventricular Tachycarida (VT) and other rhythms. It is based on time-frequency representation of the ECG and its use as input in an automatic classifier (K-nearest neighbours - KNN) without any further signal parameter extraction or additional characteristics. For comparison purposes, three time-frequency variants are analysed: pseudo Wigner-Ville representation (RTF), grey-scale image obtained from RTF (IRTF), and reduced image from IRTF (reduced IRTF). Four types of rhythms (classes) are defined: ’Normal’ for sinus rhythm, ’VT’ for ventricular tachycardia, ’VF’ for ventricular fibrillation and ’Others’ for the rest of rhythms. Classification results for VF detection in case of reduced IRTF are 88.27% sensitivity and 98.22% specificity. In case of VT, 88.31% sensitivity and 98.80% specificity is obtained, 98.14% sensitivity and 96.82% specificity for normal rhythms, and 96.91% sensitivity and 99.06% specificity for other rhythms. Finally, results are compared with other authors.

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