Short-Time Estimation of Fractionation in Atrial Fibrillation with Coarse-Grained Correlation Dimension for Mapping the Atrial Substrate

Abstract

Atrial fibrillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its first line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identification requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classification accuracy of 100% in Group 1 and 84.0-85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identified as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modification.

Keywords: atrial fibrillation; catheter ablation; chaos theory; coarse-grained correlation dimension; complex fractionated atrial electrograms; nonlinear analysis; signal processing.

Figure 1

Example of bipolar atrial fibrillation (AF) electrograms (EGMs) of different Types. AF Type IV consists of alternating Type I/II and Type III segments.

Figure 2

Example of one second segment of (i) original and (ii–iv) reconstructed AF electrograms via CGCD. (ii) Reconstructed signal with time lag $\tau =8$ ms, embedded dimension $m=4$. (iii) Reconstructed signal with time lag $\tau =8$ ms, embedded dimension $m=10$. (iv) Reconstructed signal with time lag $\tau =35$ ms, embedded dimension $m=10$. (a) AF Type I, (b) AF Type II, and (c) AF Type III. Length p of reconstructed signal decreases as $\tau$ and m increase, as can be seen from Equation (1).

Figure 3

Illustration of algorithm steps and decisions taken for AF Type IV detection on the pseudo-real recordings of Group 3 in the database.

Figure 4

Surrogate data analysis indicating coarse-grained CorDim (CGCD) values for the entire database. Values of original data are presented with a small circle, whereas surrogate values are depicted as boxplots, generated from the 40 surrogates corresponding to each time series.

Figure 5

Box plots illustrating the distribution CGCD values as a function of the AF Types, where (a) is for the most representative EGMs in Group 1, (b) for the whole database in Group 2, and (c) for Type IV pseudo-real EGMs in Group 3.

Figure 6

Receiver operating characteristics (ROC) curve analysis of discrimination between AF Types by using CGCD as a fractionation index. (a,b) Curves for the 24 most representative EGMs in Group 1 and (c,d) curves for the whole dataset analyzed in Group 2. AUC: area under the ROC curve.

Figure 7

Confusion matrices for the most representative EGMs in Group 1 (a) and the whole database in Group 2 (b). All EGMs in Group 1 were correctly classified by their AF type, whereas 17 EGMs of Group 2 were wrongly classified.

Figure 8

Decision tree together with thresholds obtained to classify EGMs by their AF Type through the application of CGCD. Scheme for the most representative EGMs in Group 1 (a) and for the whole database in Group 2 (b).

Figure 9

Scatterplots of CGCD values for the three AF Types in the most representative EGMs of Group 1 (a), in the whole database of Group 2 (b), and in Group 2 combined with the pseudo-real Type VI EGMs of Group 3 (c).