Fuzzy logic systems in cardiac dyssynchrony diagnostics
<p><strong>Aim –</strong> the development of a structural model of myocardial dyssynchrony, the construction of an artificial neural network of fuzzy inference based on this model for the diagnosis of myocardial dyssynchrony and determining the adequacy of the neural network into o...
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doaj-9b984130e75a4368b2d7c5e399b2e4242020-11-24T21:15:54ZengPC Technology CenterScienceRise2313-62862313-84162015-05-0154(10)526110.15587/2313-8416.2015.4328640890Fuzzy logic systems in cardiac dyssynchrony diagnosticsТатьяна Анатольевна Руденко0Михаил Антонович Власенко1Kharkiv Medical Academy of Postgraduate Education str. Korchagintsev, 58, Kharkiv, Ukraine, 61176Kharkiv Medical Academy of Postgraduate Education. str. Korchagintsev, 58, Kharkiv, Ukraine, 61176<p><strong>Aim –</strong> the development of a structural model of myocardial dyssynchrony, the construction of an artificial neural network of fuzzy inference based on this model for the diagnosis of myocardial dyssynchrony and determining the adequacy of the neural network into operation the automated system of diagnostics of myocardial dyssynchrony.</p><p><strong>Methods of research</strong> – simulation modeling of diagnostic system based on real data of 40 patients. The fuzzy neural network is formed as a fuzzy (linguistic) value of estimation of dyssynchrony and dyssynchrony components and defuzzificated (numeric) values of these estimations. Diagnostic hypothesis, generated by automated system diagnostics, consistent with the results of an independent analysis of patients by twelve diagnosticians (so-called method of "Committee of Experts")</p><p><strong>Results</strong> – evidence of the effectiveness of using fuzzy artificial neural network to diagnose myocardial dyssynchrony and the adequacy of the developed model of dyssynchrony.</p><p><strong>Conclusion</strong> – the automated system of myocardial dyssynchrony diagnostics based on fuzzy neural network is a useful tool for diagnostician</p>http://journals.uran.ua/sciencerise/article/view/43286myocardial dyssynchronyfuzzy logicartificial neural networkautomated systemfunctional diagnostics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Татьяна Анатольевна Руденко Михаил Антонович Власенко |
spellingShingle |
Татьяна Анатольевна Руденко Михаил Антонович Власенко Fuzzy logic systems in cardiac dyssynchrony diagnostics ScienceRise myocardial dyssynchrony fuzzy logic artificial neural network automated system functional diagnostics |
author_facet |
Татьяна Анатольевна Руденко Михаил Антонович Власенко |
author_sort |
Татьяна Анатольевна Руденко |
title |
Fuzzy logic systems in cardiac dyssynchrony diagnostics |
title_short |
Fuzzy logic systems in cardiac dyssynchrony diagnostics |
title_full |
Fuzzy logic systems in cardiac dyssynchrony diagnostics |
title_fullStr |
Fuzzy logic systems in cardiac dyssynchrony diagnostics |
title_full_unstemmed |
Fuzzy logic systems in cardiac dyssynchrony diagnostics |
title_sort |
fuzzy logic systems in cardiac dyssynchrony diagnostics |
publisher |
PC Technology Center |
series |
ScienceRise |
issn |
2313-6286 2313-8416 |
publishDate |
2015-05-01 |
description |
<p><strong>Aim –</strong> the development of a structural model of myocardial dyssynchrony, the construction of an artificial neural network of fuzzy inference based on this model for the diagnosis of myocardial dyssynchrony and determining the adequacy of the neural network into operation the automated system of diagnostics of myocardial dyssynchrony.</p><p><strong>Methods of research</strong> – simulation modeling of diagnostic system based on real data of 40 patients. The fuzzy neural network is formed as a fuzzy (linguistic) value of estimation of dyssynchrony and dyssynchrony components and defuzzificated (numeric) values of these estimations. Diagnostic hypothesis, generated by automated system diagnostics, consistent with the results of an independent analysis of patients by twelve diagnosticians (so-called method of "Committee of Experts")</p><p><strong>Results</strong> – evidence of the effectiveness of using fuzzy artificial neural network to diagnose myocardial dyssynchrony and the adequacy of the developed model of dyssynchrony.</p><p><strong>Conclusion</strong> – the automated system of myocardial dyssynchrony diagnostics based on fuzzy neural network is a useful tool for diagnostician</p> |
topic |
myocardial dyssynchrony fuzzy logic artificial neural network automated system functional diagnostics |
url |
http://journals.uran.ua/sciencerise/article/view/43286 |
work_keys_str_mv |
AT tatʹânaanatolʹevnarudenko fuzzylogicsystemsincardiacdyssynchronydiagnostics AT mihailantonovičvlasenko fuzzylogicsystemsincardiacdyssynchronydiagnostics |
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1716744188692791296 |