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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F04%3A03096799%21RIV%2F2005%2FMSM%2F212305%2FN
rdf:type
skos:Concept n12:Vysledek
dcterms:description
Není k dispozici This paper compares two different approaches to computer-aided analysis of ECG signals. ECG records are preprocessed by the wavelet transform, and the machine learning method of decision trees and fuzzy rules induction are used for classification. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. For learning and further classification we use Quinlan's See5 application and FURL (FUzzy Rule Learner). We used the MIT-BIH database for experiments. Diverse settings of the parameters for decision tree generation (tree pruning, attribute selection, class sets) were examined. Two datasets and diverse settings of fuzzysets were examined as well. This paper compares two different approaches to computer-aided analysis of ECG signals. ECG records are preprocessed by the wavelet transform, and the machine learning method of decision trees and fuzzy rules induction are used for classification. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. For learning and further classification we use Quinlan's See5 application and FURL (FUzzy Rule Learner). We used the MIT-BIH database for experiments. Diverse settings of the parameters for decision tree generation (tree pruning, attribute selection, class sets) were examined. Two datasets and diverse settings of fuzzysets were examined as well.
dcterms:title
Není k dispozici Evaluation of ECG: Comparison of Decision Tree and Fuzzy Rules Induction Evaluation of ECG: Comparison of Decision Tree and Fuzzy Rules Induction
skos:prefLabel
Není k dispozici Evaluation of ECG: Comparison of Decision Tree and Fuzzy Rules Induction Evaluation of ECG: Comparison of Decision Tree and Fuzzy Rules Induction
skos:notation
RIV/68407700:21230/04:03096799!RIV/2005/MSM/212305/N
n3:strany
713 ; 718
n3:aktivita
n16:Z
n3:aktivity
Z(MSM 210000012)
n3:dodaniDat
n10:2005
n3:domaciTvurceVysledku
n13:9431446 n13:4579577
n3:druhVysledku
n6:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
563350
n3:idVysledku
RIV/68407700:21230/04:03096799
n3:jazykVysledku
n5:eng
n3:klicovaSlova
ECG classification; decision trees; fuzzy rules learning
n3:klicoveSlovo
n4:decision%20trees n4:ECG%20classification n4:fuzzy%20rules%20learning
n3:kontrolniKodProRIV
[C5BC2333B1F7]
n3:mistoKonaniAkce
Vienna
n3:mistoVydani
Vienna
n3:nazevZdroje
Cybernetics and Systems 2004
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n10:2004
n3:tvurceVysledku
Peri, D. Lhotská, Lenka Macek, Jan
n3:typAkce
n21:WRD
n3:zahajeniAkce
2004-04-13+02:00
n3:zamer
n9:MSM%20210000012
s:numberOfPages
6
n14:hasPublisher
Austrian Society for Cybernetics Studies
n18:isbn
3-85206-169-5
n11:organizacniJednotka
21230