Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F16%3A00308362" target="_blank" >RIV/68407700:21220/16:00308362 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-49109-7_86" target="_blank" >http://dx.doi.org/10.1007/978-3-319-49109-7_86</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-49109-7_86" target="_blank" >10.1007/978-3-319-49109-7_86</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

Popis výsledku v původním jazyce

In this paper, a study and development of a monitoring adaptive system based on dynamic quadratic neural unit are presented. The system is trained with a recurrent learning method, sample-by-sample in real time. This model will help to the prediction of possible cardiac arrhythmias in patients between 23 to 89 years old, age range of the electrocardiogram signals obtained from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. By means of the implementation of this adaptive monitoring system the model is capable of processing heart rate signals in real time and to recognize patterns that predict cardiac arrhythmias up to 1 second ahead. The Dynamic Quadratic Neural Unit in real time has demonstrated presenting greater efficiency and precision comparing with multilayer perceptron-type neural networks for pattern classification and prediction; in addition, this architecture has demonstrated in developed research, to be superior to other different type of adaptive architectures.

Název v anglickém jazyce

Monitoring of Cardiac Arrhythmia Patterns by Adaptive Analysis

Popis výsledku anglicky

In this paper, a study and development of a monitoring adaptive system based on dynamic quadratic neural unit are presented. The system is trained with a recurrent learning method, sample-by-sample in real time. This model will help to the prediction of possible cardiac arrhythmias in patients between 23 to 89 years old, age range of the electrocardiogram signals obtained from the Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database. By means of the implementation of this adaptive monitoring system the model is capable of processing heart rate signals in real time and to recognize patterns that predict cardiac arrhythmias up to 1 second ahead. The Dynamic Quadratic Neural Unit in real time has demonstrated presenting greater efficiency and precision comparing with multilayer perceptron-type neural networks for pattern classification and prediction; in addition, this architecture has demonstrated in developed research, to be superior to other different type of adaptive architectures.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

3PGCIC 2016: Advances on P2P, Parallel, Grid, Cloud and Internet Computing

ISBN

978-3-319-49109-7

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

885-894

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Soonchunhyang University, Asan

Datum konání akce

5. 11. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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