Frederique Jos Vanheusden

Visualizing intracardiac atrial fibrillation electrograms using spectral analysis

Atrial fibrillation is the most common cardiac arrhythmia, and it is associated with increased risk of stroke, heart failure, and mortality. This work describes spectral analysis techniques that are being used in conjunction with visualization algorithms to help guide catheter ablation procedures that aim at treating patients with arrhythmia.

An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping

BACKGROUND AND OBJECTIVE Optimal targets for persistent atrial fibrillation (persAF) ablation are still debated. Atrial regions hosting high dominant frequency (HDF) are believed to participate in the initiation and maintenance of persAF and hence are potential targets for ablation, while rotor ablation has shown promising initial results. Currently, no commercially available system offers the capability to automatically identify both these phenomena. This paper describes an integrated 3D software platform combining the mapping of both frequency spectrum and phase from atrial electrograms (AEGs) to help guide persAF ablation in clinical cardiac electrophysiological studies. METHODS 30s of 2048 non-contact AEGs (EnSite Array, St. Jude Medical) were collected and analyzed per patient. After QRST removal, the AEGs were divided into 4s windows with a 50% overlap. Fast Fourier transform was used for DF identification. HDF areas were identified as the maximum DF to 0.25Hz below that, and their centers of gravity (CGs) were used to track their spatiotemporal movement. Spectral organization measurements were estimated. Hilbert transform was used to calculate instantaneous phase. RESULTS The system was successfully used to guide catheter ablation for 10 persAF patients. The mean processing time was 10.4 ± 1.5min, which is adequate comparing to the normal electrophysiological (EP) procedure time (120∼180min). CONCLUSIONS A customized software platform capable of measuring different forms of spatiotemporal AEG analysis was implemented and used in clinical environment to guide persAF ablation. The modular nature of the platform will help electrophysiological studies in understanding of the underlying AF mechanisms.

Investigation on recurrent high dominant frequency spatiotemporal patterns during persistent atrial fibrillation

Atrial regions hosting dominant frequency (DF) may represent potential drivers of persistent atrial fibrillation (persAF). Previous work showed that DF can exhibit cyclic behaviour. This study aims to better understand the spatiotemporal behaviours of persAF over longer time periods. 10 patients undergoing persAF ablation targeted at DF were included. Left atrial (LA) non-contact virtual electrograms (VEGMs, Ensite Array, St Jude Medical) were collected for up to 5 min pre-/post- ablation. DF was identified as the peak from 4-10 Hz, in 4 s windows (50 % overlap). High DF (HDF) map was created and automated pattern recognition algorithm was applied to look for recurring HDF spatial patterns within each patient. Recurring HDF patterns were found in all patients. Patients who changed rhythm to atrial flutter after ablation demonstrated single dominant pattern (DP) among the recorded time period, which might consistent with the higher level of regularity during flutter. Ablation regularized AF as demonstrated by increased DP recurrence after ablation. The time interval (median [IQR]) of DP recurrence for the patients still in atrial fibrillation(AF) after ablation (7 patients) decreased from 21.1 s [11.8~49.7 s] to 15.7s [6.5~18.2 s]. The proposed method quantifies the spatiotemporal regularity of HDF DPs over long time periods and may offer a more comprehensive dynamic overview of persAF behaviour and the impact of ablation.

Unifying automated fractionated atrial electrogram classification using electroanatomical mapping systems in persistent atrial fibrillation studies

Ablation targeting complex fractionated atrial electrograms (CFAE) for treating persistent atrial fibrillation (persAF) has shown conflicting results. Differences in automated algorithms embedded in NavX (St Jude Medical) and CARTO (Biosense Webster) could influence CFAE target identification for ablation, potentially affecting ablation outcomes. To evaluate this effect, automated CFAE classification performed by NavX and CARTO on the same bipolar electrograms from 18 persAF patients undergoing ablation was compared. Using the default thresholds, NavX classified 69±5% of the electrograms as CFAEs, while CARTO detected 35±5%% (Cohens kappa κ≈0.3, P<;0.0001). Both primary and complementary metrics for each system were optimized to balance CFAE detection for both systems. Using revised thresholds found from receiver operating characteristic curves, NavX classified 45±4%, while CARTO detected 42±5% (κ≈0.5, P<;0.0001). Our work takes a first step towards the optimization of CFAE detection between NavX and CARTO by providing revised thresholds to reduce differences in CFAE classification. This would facilitate direct comparisons of persAF CFAE-guided ablation outcome guided by NavX or CARTO.