Classification of aorta insufficiency and stenosis using MLP neural network and Neuro-fuzzy system


Hardalac F., BARIŞÇI N., Ergun U.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, vol.20, no.4, pp.155-165, 2004 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 4
  • Publication Date: 2004
  • Journal Name: PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.155-165
  • Gazi University Affiliated: Yes

Abstract

Cardiac Doppler signals recorded from aorta valve of 120 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of aorta valve Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and Neuro-fuzzy system inputs. in order to do a good interpretation and rapid diagnosis, (AR) parameters of aorta valve Doppler signals classified using MLP neural network and Neuro-fuzzy system. Our findings demonstrated that 90% correct classification rate was obtained from MLP neural network, and 88.33% correct classification rate was obtained from Neuro-fuzzy system. Since we had limited number of patient, there is no significant performance difference observed between the two methods.