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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Main Authors: Татьяна Анатольевна Руденко, Михаил Антонович Власенко
Format: Article
Language:English
Published: PC Technology Center 2015-05-01
Series:ScienceRise
Subjects:
Online Access:http://journals.uran.ua/sciencerise/article/view/43286
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spelling 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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