title: Classification of aorta insufficiency and stenosis using neuro-fuzzy system.
abstract: Cardiac Doppler signals recorded from aorta valve of 60 patients were transferred to a personal computer by using a 16 bit sound card. The fast Fourier transform (FFT) method was applied to the recorded signal from each patient. Since FFT method inherently cannot offer a good spectral resolution at jet blood flows such as cardiac Doppler signals, it sometimes causes wrong interpretation. In order to do a good interpretation and rapid diagnosis, cardiac Doppler blood flow signals were statistically arranged and then classified using neuro-fuzzy system. The NEFCLASS model, which is used to create a fuzzy classification system from data, was used. The classification results show that neuro-fuzzy system 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: Barşçi
authors.forename: Ergün
authors.initials: E
authors.lastname: Topal
authors.forename: Firat
authors.initials: F
authors.lastname: Hardalaç
authors.forename: Inan
authors.initials: I
authors.lastname: Güler
mesh_terms: D001022:Aortic Valve Insufficiency / Q000145:classification* / Q000000981:diagnostic imaging / Q000503:physiopathology; D001024:Aortic Valve Stenosis / Q000145:classification* / Q000000981:diagnostic imaging / Q000503:physiopathology; D001783:Blood Flow Velocity; D003936:Diagnosis, Computer-Assisted; D015150:Echocardiography, Doppler; D005583:Fourier Analysis; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer