Classification of aorta insufficiency and stenosis using neuro-fuzzy system

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...

Full description

Saved in:
Bibliographic Details
Published in:Journal of medical systems 2005-04, Vol.29 (2), p.155-165
Main Authors: Barşçi, Necaattin, Topal, Ergün, Hardalaç, Firat, Güler, Inan
Format: Article
Language:English
Subjects:
Aortic Valve Insufficiency - classification
Aortic Valve Insufficiency - diagnostic imaging
Aortic Valve Insufficiency - physiopathology
Aortic Valve Stenosis - classification
Aortic Valve Stenosis - diagnostic imaging
Aortic Valve Stenosis - physiopathology
Blood Flow Velocity
Classification
Computer applications
Diagnosis, Computer-Assisted
Echocardiography, Doppler
Fourier Analysis
Fourier transforms
Fuzzy Logic
Health care
Heart disease
Humans
Medicine
Neural networks
Neural Networks (Computer)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0148-5598
1573-689X
DOI:10.1007/s10916-005-3003-9