Abstract: O. Ozgul, B. Maesen, U. Schotten, P. Bonizzi, S. Zeemering (2021)

Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021), 2021, 508–511p. (2021) DOI:10.1109/EMBC46164.2021.9630766

State Space Embedding of Atrial Electrograms to Detect Repetitive Conduction Patterns During Atrial Fibrillation

Repetitive atrial conduction patterns are often observed during human atrial fibrillation (AF). Repetitive patterns may be associated with AF drivers such as focal and micro-reentrant mechanisms. Therefore, tools for repetitive activity detection are of great interest as they may allow to identify the leading electrophysiological AF mechanism in an individual patient. Recurrence plots (RP) are efficient tools for repetitive activity visualization. Construction of an RP requires embedding of epicardial atrial electrograms into a phase space. In this study, we compared the conventional Takens' embedding approach and three novel approaches based on a priori AF cycle length (AFCL) information. Approaches were bench-marked based on the similarity of the RPs they produce with a previously proposed technique, the sensitivity and specificity to detect the repetitive patterns, as well as the capability to estimate overall repetitive pattern prevalence. All techniques were applied to high-density epicardial direct contact mapping recordings in AF patients with paroxysmal AF (n=12) and persistent AF (n=9). Compared to a reference method the proposed novel techniques achieved significantly higher similarity and sensitivity values (p<0.01) than conventional embedding, in both paroxysmal and persistent patients. Moreover, estimated prevalences were robust against the various degrees of AF complexity present in the recordings.Clinical relevance- This study presents three novel approaches for detection of repetitive patterns of conduction during AF in atrial direct contact recordings, which may aid in the identification of the leading AF mechanism in an individual patient.

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