ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00158541" target="_blank" >RIV/68407700:21230/09:00158541 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window

Popis výsledku v původním jazyce

In this work we investigate the use of wavelet packets transform as a mean to extract features capable of providing the information needed by a classifier for discrimination between normal beats (N), premature ventricular (V), left bundle branch block beat 'L' and right bundle branch block beat 'R'. We have used our approach of feature extraction which is based on wavelet packets decomposition of window with QRS complex and on the method of wavelet packet tree node selection. This selection has been performed as finding of the path with maximum of relative entropy between current beat and template. We have used the MIT-BIH arrhythmia database for testing this approach. The database was divided into two subsets for training and testing the classifier. Results of the classification are evaluated by the sensitivity (Se), specificity (Sp) and overall accuracy. We obtained sensitivity 81,1%, 98,5%, specificity 95,1% and 97,5% overall accuracy for ventricular beat.

Název v anglickém jazyce

ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window

Popis výsledku anglicky

In this work we investigate the use of wavelet packets transform as a mean to extract features capable of providing the information needed by a classifier for discrimination between normal beats (N), premature ventricular (V), left bundle branch block beat 'L' and right bundle branch block beat 'R'. We have used our approach of feature extraction which is based on wavelet packets decomposition of window with QRS complex and on the method of wavelet packet tree node selection. This selection has been performed as finding of the path with maximum of relative entropy between current beat and template. We have used the MIT-BIH arrhythmia database for testing this approach. The database was divided into two subsets for training and testing the classifier. Results of the classification are evaluated by the sensitivity (Se), specificity (Sp) and overall accuracy. We obtained sensitivity 81,1%, 98,5%, specificity 95,1% and 97,5% overall accuracy for ventricular beat.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

<a href="/cs/project/1ET201210527" target="_blank" >1ET201210527: Znalostní podpora diagnostiky a predikce v kardiologii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název statě ve sborníku

World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany

ISBN

978-3-642-03897-6

ISSN

1680-0737

e-ISSN

—

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

Springer Science+Business Media

Místo vydání

Berlin

Místo konání akce

Mnichov

Datum konání akce

7. 9. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—