Classification of Mitral Insufficiency and Stenosis Using MLP Neural Network and Neuro–Fuzzy System

Authors:
Necaattin Barý/þ/ç/ý/;Uç/man Ergü/n;Erdoğ/an İ/lkay;Selami Serhatlý/oð/lu;Fı/rat Hardalaç/;İ/nan Gü/ler
Affiliations:
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Cardiology, Faculty of Medicine, Fı/rat University, Elazı/ğ/, Turkey;Department of Radiology, Faculty of Medicine, Fı/rat University, Elazı/ğ/, Turkey;Department of Biophysics, Faculty of Medicine, Fı/rat University, Elazı/ğ/, Turkey/ firat@gazi.edu.tr;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2004

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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.