Review on Higher-Order Neural Units to Monitor Cardiac Arrhythmia Patterns

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F17%3A00315048" target="_blank" >RIV/68407700:21220/17:00315048 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3233/978-1-61499-773-3-219" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-773-3-219</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-773-3-219" target="_blank" >10.3233/978-1-61499-773-3-219</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Review on Higher-Order Neural Units to Monitor Cardiac Arrhythmia Patterns

Popis výsledku v původním jazyce

An electrocardiogram (ECG) is a non-invasive technique that checks for problems with the electrical activity of a patient’s heart. ECG is economical and extremely versatile. Some of its characteristics make it a very useful tool to detect cardiac pathologies. The ECG records a series of characteristic waves called PQRST; however, the QRS complex analysis enables the detection of a type of arrhythmia in an ECG. Technological developments enable the storage of a large amount of data, from which knowledge extraction is impossible without a powerful data processing tool; in particular, an adequate signal processing tool, whose output provides reliable parameters as a basis to make a precise clinical diagnosis. Thus, ECG signal processing creates an opportunity to analyze and recognize possible arrhythmia patterns. This paper reviews the use of artificial neural networks (ANNs) to detect and recognize cardiac arrhythmia patterns. Recurrent neural networks (RNNs) and higher-order neural units are inspected. In addition, the potentials of using higher-order neural units such as the quadratic dynamic neural unit (D-QNU) and dynamic cubic neural unit (D-CNU) for cardiac arrhythmia detection are analyzed.

Název v anglickém jazyce

Review on Higher-Order Neural Units to Monitor Cardiac Arrhythmia Patterns

Popis výsledku anglicky

An electrocardiogram (ECG) is a non-invasive technique that checks for problems with the electrical activity of a patient’s heart. ECG is economical and extremely versatile. Some of its characteristics make it a very useful tool to detect cardiac pathologies. The ECG records a series of characteristic waves called PQRST; however, the QRS complex analysis enables the detection of a type of arrhythmia in an ECG. Technological developments enable the storage of a large amount of data, from which knowledge extraction is impossible without a powerful data processing tool; in particular, an adequate signal processing tool, whose output provides reliable parameters as a basis to make a precise clinical diagnosis. Thus, ECG signal processing creates an opportunity to analyze and recognize possible arrhythmia patterns. This paper reviews the use of artificial neural networks (ANNs) to detect and recognize cardiac arrhythmia patterns. Recurrent neural networks (RNNs) and higher-order neural units are inspected. In addition, the potentials of using higher-order neural units such as the quadratic dynamic neural unit (D-QNU) and dynamic cubic neural unit (D-CNU) for cardiac arrhythmia detection are analyzed.

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

13

Strana od-do

219-231

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

000440621900020