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 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Güven, Ayşegül Dirgenali, Fatma Okandan, Mustafa Kara, Sadık |
| Author_xml | – sequence: 1 givenname: Sadık surname: Kara fullname: Kara, Sadık email: kara@erciyes.edu.tr organization: Department of Electronics Engineering, Biomedical Eng. Group, Erciyes University, 38039 Kayseri, Turkey – sequence: 2 givenname: Ayşegül surname: Güven fullname: Güven, Ayşegül email: aguven@erciyes.edu.tr organization: Department of Electronics, Civil Aviation School, Erciyes University, 38039 Kayseri, Turkey – sequence: 3 givenname: Mustafa surname: Okandan fullname: Okandan, Mustafa organization: Department of Electronics Engineering, Biomedical Eng. Group, Erciyes University, 38039 Kayseri, Turkey – sequence: 4 givenname: Fatma surname: Dirgenali fullname: Dirgenali, Fatma email: fdirgenali@tse.org.tr organization: Department of Electrical Engineering, 38039 Kayseri, Turkey |
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| Cites_doi | 10.1109/RCEMBS.1995.532173 10.1016/S0301-5629(97)00011-2 10.1109/10.563293 10.1148/radiology.192.3.7914706 10.1016/0301-5629(90)90089-U 10.1108/02602289710163355 10.1016/S0140-6736(95)92904-5 10.1016/S0957-4174(02)00042-8 10.1016/S0140-6736(95)91804-3 10.1016/S0010-4825(03)00011-8 10.1142/S0218488595000219 10.1016/0301-5629(89)90070-7 10.1109/ICASSP.1977.1170219 10.1088/0967-3334/14/1/003 10.1016/S0010-4825(96)00018-2 10.1016/S0301-5629(99)00015-0 10.1007/BF02457822 10.1162/neco.1990.2.4.480 |
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| Keywords | Mitral valve stenosis Autoregressive modeling Back propagation algorithm Levenberg Marquart method Artificial neural networks |
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| SubjectTerms | 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 |
| Title | Utilization of artificial neural networks and autoregressive modeling in diagnosing mitral valve stenosis |
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