Classification of Aorta Insufficiency and Stenosis Using Neuro-Fuzzy System

Authors:
Necaattin Barzşçi;Ergün Topal;Fzrat Hardalaç;Īnan Güler
Affiliations:
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey;Department of Cardiology, Faculty of Medicine, İnönü University, Malatya, Turkey;Aff3 Aff4;Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey
Venue:
Journal of Medical Systems
Year:
2005

Citing 5
Cited 1

Fuzzy neural networks: a survey

Fuzzy Sets and Systems
Neural networks in designing fuzzy systems for real world applications

Fuzzy Sets and Systems
A neuro-fuzzy method to learn fuzzy classification rules from data

Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data

SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Foundations of Neuro-Fuzzy Systems

Foundations of Neuro-Fuzzy Systems

Correlation Dimension Analysis of Doppler Signals in Children with Aortic Valve Disorders

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

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.