Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system.
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.
Barýpçý
Necaattin
N
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
Ergün
Uçman
U
Ilkay
Erdoğan
E
Serhatlýoğlu
Selami
S
Hardalaç
Firat
F
Güler
Inan
I
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015150:Echocardiography, Doppler; D017143:Fuzzy Logic; D006801:Humans; D008944:Mitral Valve Insufficiency / Q000145:classification* / Q000000981:diagnostic imaging; D008946:Mitral Valve Stenosis / Q000145:classification* / Q000000981:diagnostic imaging; D014481:United States