Classification of cardiac excitation patterns during atrial fibrillation

Abstract: The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Classification of cardiac excitation patterns during atrial fibrillation ; volume:2 ; number:1 ; year:2016 ; pages:161-166 ; extent:6 ; Current directions in biomedical engineering ; 2, Heft 1 (2016), 161-166 (gesamt 6)

Beteiligte, Orts- und Zeitangaben

• Urheber: Reich, Christian ; Oesterlein, Tobias ; Rottmann, Markus ; Seemann, Gunnar ; Dössel, Olaf

Weitere Informationen

• DOI: 10.1515/cdbme-2016-0037
• URN: urn:nbn:de:101:1-2410141611422.777520905665
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:26 MESZ