Cycle Length Evaluation in Persistent Atrial Fibrillation Using Kernel Density Estimation to Identify Transient and Stable Rapid Atrial Activity

Purpose Left atrial (LA) rapid AF activity has been shown to co-localise with areas of successful atrial fibrillation termination by catheter ablation. We describe a technique that identifies rapid and regular activity. Methods Eight-second AF electrograms were recorded from LA regions during ablation for psAF. Local activation was annotated manually on bipolar signals and where these were of poor quality, we inspected unipolar signals. Dominant cycle length (DCL) was calculated from annotation pairs representing a single activation interval, using a probability density function (PDF) with kernel density estimation. Cumulative annotation duration compared to total segment length defined electrogram quality. DCL results were compared to dominant frequency (DF) and averaging. Results In total 507 8 s AF segments were analysed from 7 patients. Spearman’s correlation coefficient was 0.758 between independent annotators (P < 0.001), 0.837–0.94 between 8 s and ≥ 4 s segments (P < 0.001), 0.541 between DCL and DF (P < 0.001), and 0.79 between DCL and averaging (P < 0.001). Poorer segment organization gave greater errors between DCL and DF. Conclusion DCL identifies rapid atrial activity that may represent psAF drivers. This study uses DCL as a tool to evaluate the dynamic, patient specific properties of psAF by identifying rapid and regular activity. If automated, this technique could rapidly identify areas for ablation in psAF.

CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution

BackgroundRate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity.Methods and ResultsThe S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude.ConclusionThe proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.

Abstract 14839: High-resolution Spatiotemporal Changes in Dominant Frequency and Structural Organization During Persistent Atrial Fibrillation

Introduction: Spatiotemporal differences in atrial activity are thought to contribute to the maintenance of atrial fibrillation (AF). While recent evidence has identified changes in dominant frequency (DF) during the transition from paroxysmal to persistent AF, little is known about the frequency characteristics of the epicardium during this transition. The purpose of this study was to perform high-resolution mapping of the atrial epicardium and to characterize changes in frequency activity and structural organization during the transition from paroxysmal to persistent AF. Hypothesis: In a porcine model of persistent AF, we tested the hypothesis that the epicardium undergoes spatiotemporal changes in atrial activity and structural organization during persistent AF. Methods: Paroxysmal and persistent AF was induced in adult Yorkshire swine by atrial tachypacing. Atrial morphology was segmented from magnetic resonance imaging and high-resolution patient-specific flexible mapping arrays were 3D printed to match the epicardial contours of the atria. Epicardial activation and DF mapping was performed in four paroxysmal and four persistent AF animals using personalized mapping arrays. Histological analysis was performed to determine structural differences between paroxysmal and persistent AF. Results: The left atrial epicardium was associated with a significant increase in DF between paroxysmal and persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). High-resolution spatiotemporal mapping identified organized clusters of DF during paroxysmal AF which were lost during persistent AF. The development of persistent AF led to structural remodeling with increased atrial epicardial fibrosis. The organization index (OI) significantly decreased during persistent AF in both the left atria (0.3 ± 0.03 vs. 0.2 ± 0.03, P = 0.01) and right atria (0.33 ± 0.04 vs. 0.23 ± 0.02, P = 0.02). Conclusions: In the porcine model of persistent AF, the epicardium undergoes structural remodeling with increased epicardial fibrosis, reflected by changes in atrial organization index and dominant frequency.

Left But Not Right Ventricular Abnormal Doppler Waves

The presence of a normal atrial electrical activity together with the absence of mechanical atrial activity has been reported after successful cardioversion and is named atrial stunning. In this observation, we report the existence of left but not right atrial stunning.