. "Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier" . . . "Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier"@en . . "339569" . "Bratislava" . "[0D272F4121E4]" . "Proceedings Measurement 2009" . . . . . . . . "Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier" . "atrial fibrillation; complex fractionated electrograms; signal processing; pattern recognition; feature extraction; classification"@en . "2009-05-20+02:00"^^ . "\u00DAstav merania SAV" . . "2"^^ . . "RIV/68407700:21230/09:00163441!RIV10-MSM-21230___" . . . "K\u0159emen, V\u00E1clav" . "2"^^ . . . "978-80-969672-1-6" . "Lhotsk\u00E1, Lenka" . "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."@en . "Smolenice Castle" . "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." . . "Z(MSM6840770012)" . "21230" . . "Robust Classification of Endocardial Electrograms Fractionation in Human using Nearest Mean Classifier"@en . "RIV/68407700:21230/09:00163441" . "4"^^ . .