Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation. (July 2019)

• Record Type: Journal Article
• Authors: Acharya, U. Rajendra; Faust, Oliver; Ciaccio, Edward J.; Koh, Joel En Wei; Oh, Shu Lih; Tan, Ru San; Garan, Hasan
• Is Part Of: Computer methods and programs in biomedicine. Volume 175(2019)
• Page Start: 163
• Page End: 178
• Publication Date: 2019-07
• Subjects: Electrogram -- Recurrence plot -- Recurrence quantification analysis -- Entropy measures; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.04.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: What did you do? We have extracted information about atrial fibrillation from complex fractionated atrial electrograms. How did you do it? We have processed the signals with recurrence plots and associated feature extraction methods. What did you learn? Short data sequences are sufficient to provide information to discern persistent versus paroxysmal atrial fibrillation data with a significant difference, and can be useful to detect repeating patterns of atrial activation. Abstract: Background and Objective: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE. Methods: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures. Results: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data. Conclusions: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.