Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system

J Med Syst. 2004 Oct;28(5):423-36. doi: 10.1023/b:joms.0000041169.28544.fd.

Abstract

Cardiac Doppler signals recorded from mitral valve of 60 patients were transferred to a personal computer by using a 16-bit sound card. The power spectral density (PSD) was applied to the recorded signal from each patient. In order to do a good interpretation and rapid diagnosis, PSD values classified using multilayer perceptron (MLP) and neuro-fuzzy system. Our findings demonstrated that 93.33% classification success rate was obtained from MLP, 90% classification success rate was obtained from neuro-fuzzy system. The classification results show that MLP offers best results in the case of diagnosis.

MeSH terms

  • Diagnosis, Computer-Assisted / methods*
  • Echocardiography, Doppler
  • Fuzzy Logic*
  • Humans
  • Mitral Valve Insufficiency / classification*
  • Mitral Valve Insufficiency / diagnostic imaging
  • Mitral Valve Stenosis / classification*
  • Mitral Valve Stenosis / diagnostic imaging
  • United States