Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis

This research is concentrated on the diagnosis of mitral heart valve stenosis through the analysis of Doppler Signals’ AR power spectral density graphic with the help of ANN. Multilayer feedforward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented in the MATLAB environm...

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Veröffentlicht in:Computers in biology and medicine Jg. 36; H. 5; S. 473 - 483
Hauptverfasser: Kara, Sadık, Güven, Ayşegül, Okandan, Mustafa, Dirgenali, Fatma
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.05.2006
Schlagworte:
Adult
Algorithms
Artificial Intelligence
Artificial neural networks
Autoregressive modeling
Back propagation algorithm
Computer Simulation
Constriction, Pathologic - pathology
False Positive Reactions
Female
Humans
Levenberg Marquart method
Male
Mitral valve stenosis
Mitral Valve Stenosis - diagnosis
Mitral Valve Stenosis - pathology
Neural Networks (Computer)
Pattern Recognition, Automated
Regression Analysis
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Mitral valve stenosis
Autoregressive modeling
Back propagation algorithm
Levenberg Marquart method
Artificial neural networks
ISSN:0010-4825, 1879-0534
Online-Zugang:Volltext
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Zusammenfassung:This research is concentrated on the diagnosis of mitral heart valve stenosis through the analysis of Doppler Signals’ AR power spectral density graphic with the help of ANN. Multilayer feedforward ANN trained with a Levenberg Marquart backpropagation algorithm was implemented in the MATLAB environment. Correct classification of 94% was achieved, whereas 4 false classifications have been observed for the test group of 68 subjects in total. The designed classification structure has about 97.3% sensitivity, 90.3% specifity and positive prediction is calculated to be 92.3%. The stated results show that the proposed method can make an effective interpretation.
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ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2005.01.007