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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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. 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. (en)
Title	ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window (en)
skos:prefLabel	ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window (en)
skos:notation	RIV/68407700:21230/09:00158541!RIV10-AV0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(1ET201210527)
http://linked.open...vai/riv/dodaniDat	2010
http://linked.open...aciTvurceVysledku	Huptych, Michal Lhotská, Lenka
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	311802
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/09:00158541
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	wavelet transform; wavelet packets; feature extraction; local discriminant bases; ECG; arrhythmias (en)
http://linked.open.../riv/klicoveSlovo	wavelet transform local discriminant bases wavelet packets ECG arrhythmias feature extraction
http://linked.open...ontrolniKodProRIV	[861D18983F68]
http://linked.open...v/mistoKonaniAkce	Mnichov
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Knowledge-based support of diagnostics and prediction in cardiology
http://linked.open...UplatneniVysledku	2009
http://linked.open...iv/tvurceVysledku	Huptych, Michal Lhotská, Lenka
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2009-09-07 (xsd:date)
issn	1680-0737
number of pages	4 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer Science+Business Media
https://schema.org/isbn	978-3-642-03897-6
http://localhost/t...ganizacniJednotka	21230
is http://linked.open...avai/riv/vysledek of	ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software