Discriminating between V and N Beats from ECGs Introducing an Integrated Reduced Representation along with a Neural Network Classifier

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00203834" target="_blank" >RIV/68407700:21230/09:00203834 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-642-04277-5_49#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-642-04277-5_49#page-1</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Discriminating between V and N Beats from ECGs Introducing an Integrated Reduced Representation along with a Neural Network Classifier

Popis výsledku v původním jazyce

The main objective of this paper is to investigate and propose a new approach to distinguish between two classes of beats from the ECG holter recordings - the premature ventricular beats (V) and the normal ones (N). The integrated methodology consists ofa specific sequence: R-peak detection, feature extraction, Principal Component Analysis dimensionality reduction and classification with a neural classifier. ECG heats of hotter recordings are described using means as simple as possible resulting in a description of the QRS complex by features derived mathematically from the signal using only R-peak detection. For this research work, normal (N) and ventricular (V) beats from the well known MIT-BIH database were used to test the proposed methodology. The results are promising paving the way for the more demanding multiclass classification problem.

Název v anglickém jazyce

Discriminating between V and N Beats from ECGs Introducing an Integrated Reduced Representation along with a Neural Network Classifier

Popis výsledku anglicky

The main objective of this paper is to investigate and propose a new approach to distinguish between two classes of beats from the ECG holter recordings - the premature ventricular beats (V) and the normal ones (N). The integrated methodology consists ofa specific sequence: R-peak detection, feature extraction, Principal Component Analysis dimensionality reduction and classification with a neural classifier. ECG heats of hotter recordings are described using means as simple as possible resulting in a description of the QRS complex by features derived mathematically from the signal using only R-peak detection. For this research work, normal (N) and ventricular (V) beats from the well known MIT-BIH database were used to test the proposed methodology. The results are promising paving the way for the more demanding multiclass classification problem.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Neural Networks - ICANN 2009 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II

ISBN

978-3-642-04276-8

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

10

Strana od-do

485-494

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Limassol

Datum konání akce

14. 9. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000275896800049