The GAME Algorithm Applied to Complex Fractionated Atrial Electrograms Data Set
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03147064" target="_blank" >RIV/68407700:21230/08:03147064 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
The GAME Algorithm Applied to Complex Fractionated Atrial Electrograms Data Set
Original language description
Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). To identify CFAEs sites, we developed algorithm based on wavelet transform allowing automated feature extraction from source signals. Signals were ranked by three experts into four classes. We compiled a representative data set of 113 instances with extracted features as inputs and average of expert ranking as the output. In this paper, we present results of our GAME data mining algorithm, that was used to (a) predict average ranking of experts, (b) classify into three classes. Our results indicate that wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate and that the GAME algorithm outperforms other data mining techniques (such as decision trees, linear regression, neural networks, Support Vector Machines, etc.) in both prediction and classification accuracy.
Czech name
The GAME Algorithm Applied to Complex Fractionated Atrial Electrograms Data Set
Czech description
Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). To identify CFAEs sites, we developed algorithm based on wavelet transform allowing automated feature extraction from source signals. Signals were ranked by three experts into four classes. We compiled a representative data set of 113 instances with extracted features as inputs and average of expert ranking as the output. In this paper, we present results of our GAME data mining algorithm, that was used to (a) predict average ranking of experts, (b) classify into three classes. Our results indicate that wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate and that the GAME algorithm outperforms other data mining techniques (such as decision trees, linear regression, neural networks, Support Vector Machines, etc.) in both prediction and classification accuracy.
Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/KJB201210701" target="_blank" >KJB201210701: Automated Knowledge Extraction</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks - ICANN 2008
ISBN
978-3-540-87558-1
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Springer
Place of publication
Heidelberg
Event location
Prague
Event date
Sep 3, 2008
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000259567200089