Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system
Related Experiment Videos
Necaattin Barýpçý1, Uçman Ergün, Erdoğan Ilkay
1Department of Electronic and Computer Education, Faculty of Technical Education, Gazi University, Ankara, Turkey.
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.
Related Concept Videos
Mitral Stenosis III: Medical Management
Mitral Valve Prolapse II: Assessment and Management
Mitral Valve Prolapse I: Introduction
Mitral Stenosis I: Introduction
Mitral Stenosis II: Clinical features and Diagnostic Tests
Mitral Valve Prolapse III: Nursing Management