title: Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system.
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.
authors.affiliation: Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
authors.forename: Necaattin
authors.initials: N
authors.lastname: Barýpçý
authors.forename: Uçman
authors.initials: U
authors.lastname: Ergün
authors.forename: Erdoğan
authors.initials: E
authors.lastname: Ilkay
authors.forename: Selami
authors.initials: S
authors.lastname: Serhatlýoğlu
authors.forename: Firat
authors.initials: F
authors.lastname: Hardalaç
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
mesh_terms: 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