Documento de conferencia

Knowledge based refining of K-NN fuzzy classifier: A case study in ventricular arrhythmia diagnosis

  • D. Cabello /
  • S. Barro /
  • J. M. Salceda /
  • R. Ruiz /
  • J. Mira
Conference Proceeding cp
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
  • Volumen: 11 pt 1
  • Fecha: 01 noviembre 1989
  • Páginas: 145-146
  • ISSN: 05891019
  • Tipo de fuente: Ponencia
  • Tipo de documento: Documento de conferencia
  • Editorial: Publ by Alliance for Engineering in Medicine & Biology Washington, DC, United States
A process for the detection of lethal ventricular arrhythmias is presented. This consists of the refinement, based on knowledge, of the confidence factors (membership functions) in the classification provided by a K-NN fuzzy statistical classifier. This classifier differentiates, from a set of spectral parameters obtained from segments of the electrocardiographic signal, between ventricular flutter-fibrillation, ventricular arrhythmias with complexes of aberrant morphology, and artifacts imitating both categories. In this context, one looks for the evidence that complements the confidence factors, from the initial statistical classification, in the behavior of the pressure in the pulmonary artery.

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