Document Detail


Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system.
MedLine Citation:
PMID:  15527030     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Necaattin Barýpçý; Uçman Ergün; Erdoğan Ilkay; Selami Serhatlýoğlu; Firat Hardalaç; Inan Güler
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of medical systems     Volume:  28     ISSN:  0148-5598     ISO Abbreviation:  J Med Syst     Publication Date:  2004 Oct 
Date Detail:
Created Date:  2004-11-05     Completed Date:  2005-02-24     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7806056     Medline TA:  J Med Syst     Country:  United States    
Other Details:
Languages:  eng     Pagination:  423-36     Citation Subset:  IM    
Affiliation:
Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
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MeSH Terms
Descriptor/Qualifier:
Diagnosis, Computer-Assisted / methods*
Echocardiography, Doppler
Fuzzy Logic*
Humans
Mitral Valve Insufficiency / classification*,  ultrasonography
Mitral Valve Stenosis / classification*,  ultrasonography
United States

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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