Estimation of Sympathetic and Parasympathetic Level during Orthostatic Stress using Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14710%2F09%3A00043002" target="_blank" >RIV/00216224:14710/09:00043002 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of Sympathetic and Parasympathetic Level during Orthostatic Stress using Artificial Neural Networks

Popis výsledku v původním jazyce

This study deals with the development of a new method to quantify the effect of orthostatic stress on the cardiovascular system. Orthostatic hypotension in healthy subjects triggers the baroreflex, which induces increased sympathetic activity and decreased parasympathetic activity. We performed a tilt-table test on 19 healthy subjects while measuring electrocardiogram, galvanic skin resistance and blood pressure signals. We developed a method for inverse parameters identification using artificial neuralnetworks to fit the experimental data and identify physiological parameters (sympathetic and parasympathetic level). We implemented a supervised controller in the form of mathematical model of the baroreflex which was used to estimate the sympathetic and parasympathetic levels for a selected set of experimental data. Obtained result was used as training set for our artificial neural network. The network was able to estimate the levels of sympathetic and parasympathetic discharge.

Název v anglickém jazyce

Estimation of Sympathetic and Parasympathetic Level during Orthostatic Stress using Artificial Neural Networks

Popis výsledku anglicky

This study deals with the development of a new method to quantify the effect of orthostatic stress on the cardiovascular system. Orthostatic hypotension in healthy subjects triggers the baroreflex, which induces increased sympathetic activity and decreased parasympathetic activity. We performed a tilt-table test on 19 healthy subjects while measuring electrocardiogram, galvanic skin resistance and blood pressure signals. We developed a method for inverse parameters identification using artificial neuralnetworks to fit the experimental data and identify physiological parameters (sympathetic and parasympathetic level). We implemented a supervised controller in the form of mathematical model of the baroreflex which was used to estimate the sympathetic and parasympathetic levels for a selected set of experimental data. Obtained result was used as training set for our artificial neural network. The network was able to estimate the levels of sympathetic and parasympathetic discharge.

Druh

D - Stať ve sborníku

CEP obor

FS - Lékařská zařízení, přístroje a vybavení

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Recent Advances in Mechatronics

ISBN

978-3-642-05021-3

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Luhačovice

Datum konání akce

18. 11. 2009

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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