Adaptive Methodology for Designing a Predictive Model of Cardiac Arrhythmia Symptoms Based on Cubic Neural Unit

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F17%3A00312426" target="_blank" >RIV/68407700:21220/17:00312426 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/978-1-61499-773-3-232" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-773-3-232</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-773-3-232" target="_blank" >10.3233/978-1-61499-773-3-232</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive Methodology for Designing a Predictive Model of Cardiac Arrhythmia Symptoms Based on Cubic Neural Unit

Popis výsledku v původním jazyce

A cubic neural unit is a kind of a higher-order neural unit which can be used for prediction tasks among others, in the medical field. The example of the tasks includes monitoring cardiac behavior in real-time either for preemptive treatment, or for supporting a doctor to reach a more accurate diagnosis. We propose a predictive model which has been developed as an application in open source code with the aim to make it publicly accessible for research community and medical professionals and also to decrease the implementation cost. The proposed model uses sample-by-sample adaptation of the gradient descent method with error backpropagation. This paper presents an application of a cubic neural unit as a prediction mechanism for abnormal cardiac behavior, and it describes a new adaptive methodology based on application of a dynamic cubic neural unit for cardiac arrhythmia prediction. To validate the model, it has been tested on the data from the Massachusetts Institute of Technology-Beth Israel Hospital Cardiac Record Database. This paper is focused on premature ventricular contraction, atrial premature contraction and normal heartbeat records

Název v anglickém jazyce

Adaptive Methodology for Designing a Predictive Model of Cardiac Arrhythmia Symptoms Based on Cubic Neural Unit

Popis výsledku anglicky

A cubic neural unit is a kind of a higher-order neural unit which can be used for prediction tasks among others, in the medical field. The example of the tasks includes monitoring cardiac behavior in real-time either for preemptive treatment, or for supporting a doctor to reach a more accurate diagnosis. We propose a predictive model which has been developed as an application in open source code with the aim to make it publicly accessible for research community and medical professionals and also to decrease the implementation cost. The proposed model uses sample-by-sample adaptation of the gradient descent method with error backpropagation. This paper presents an application of a cubic neural unit as a prediction mechanism for abnormal cardiac behavior, and it describes a new adaptive methodology based on application of a dynamic cubic neural unit for cardiac arrhythmia prediction. To validate the model, it has been tested on the data from the Massachusetts Institute of Technology-Beth Israel Hospital Cardiac Record Database. This paper is focused on premature ventricular contraction, atrial premature contraction and normal heartbeat records

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 8th International Conference on Applications of Digital Information and Web Technologies

ISBN

978-1-61499-772-6

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

232-239

Název nakladatele

IOS Press BV

Místo vydání

Amsterdam

Místo konání akce

Juarez City

Datum konání akce

29. 3. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000440621900021