Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00163441" target="_blank" >RIV/68407700:21230/09:00163441 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier
Original language description
Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. A novel algorithm for automated description of atrial electrograms (A-EGMs) fractionation based on wavelet transform and several statistical pattern recognition methods was proposed and new methodology of A-EGM processing was designed and tested in such a comprehensive form than ever before. The algorithms for signal processing, description and classification were developed and validated using a representative set of 1:5 s A-EGMs (n = 113) ranked by 3 independent experts into 4 classes of fractionation: 1- organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation.
Czech name
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Czech description
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Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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