A novel approach to propagation pattern analysis in intracardiac atrial fibrillation signals.
The purpose of this study is to investigate propagation patterns in intracardiac signals recorded during atrial fibrillation (AF) using an approach based on partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The PDC is evaluated at the dominant frequency of AF signals and tested for significance using a surrogate data procedure specifically designed to assess causality. For significantly coupled sites, the approach allows also to estimate the delay in propagation. The methods potential is illustrated with two simulation scenarios based on a detailed ionic model of the human atrial myocyte as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. In both simulation scenarios the significant PDCs correctly reflect the direction of coupling and thus the propagation between all recording sites. In the real data recordings, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF, which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of arrhythmia ablation.
Richter
Ulrike
U
Department of Electrical and Information Technology, and Center for Integrative Electrocardiology (CIEL), Lund University, Box 118, 221 00, Lund, Sweden. urr@eit.lth.se
Faes
Luca
L
Cristoforetti
Alessandro
A
Masè
Michela
M
Ravelli
Flavia
F
Stridh
Martin
M
Sörnmo
Leif
L
D000200:Action Potentials; D001281:Atrial Fibrillation / Q000503:physiopathology*; D018780:Body Surface Potential Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006329:Heart Conduction System / Q000503:physiopathology*; D006801:Humans; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*