A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins.

BACKGROUND: Superimposition of farfield (FF) and nearfield (NF) bipolar voltage electrograms (BVE) complicates the confirmation of pulmonary vein (PV) isolation after catheter ablation of atrial fibrillation. Our aim was to develop an automatic algorithm based on a single-beat analysis to discriminate PV NF from atrial FF BVE from a circular mapping catheter during the cryoballoon PV isolation. METHODS: During freezing cycles in cryoablation PVI, local NF and distant FF signals were recorded, identified and labelled. BVEs were classified using four different machine learning algorithms based on four frequency domain (high-frequency power (PHF), low-frequency power (PLF), relative high power band, PHF ratio of neighbouring electrodes) and two time domain features (amplitude (Vmax), slew rate). The algorithm-based classification was compared to the true identification gained during the PVI and to a classification by cardiac electrophysiologists. RESULTS: We included 335 BVEs from 57 consecutive patients. Using a single feature, PHF with a cut-off at 150 Hz showed the best overall accuracy for classification (79.4%). By combining PHF with Vmax, overall accuracy was improved to 82.7% with a specificity of 89% and a sensitivity of 77%. The overall accuracy was highest for the right inferior PV (96.6%) and lowest for the left superior PV (76.9%). The algorithm showed comparable accuracy to the classification by the EP specialists. CONCLUSIONS: An automated farfield-nearfield discrimination based on two simple features from a single-beat BVE is feasible with a high specificity and comparable accuracy to the assessment by experienced cardiac electrophysiologists.

Evaluation and optimization of novel extraction algorithms for the automatic detection of atrial activations recorded within the pulmonary veins during atrial fibrillation.

BACKGROUND AND OBJECTIVE: The automated detection of atrial activations (AAs) recorded from intracardiac electrograms (IEGMs) during atrial fibrillation (AF) is challenging considering their various amplitudes, morphologies and cycle length. Activation time estimation is further complicated by the constant changes in the IEGM active zones in complex and/or fractionated signals. We propose a new method which provides reliable automatic extraction of intracardiac AAs recorded within the pulmonary veins during AF and an accurate estimation of their local activation times. METHODS: First, two recently developed algorithms were evaluated and optimized on 118 recordings of pulmonary vein IEGM taken from 35 patients undergoing ablation of persistent AF. The adaptive mathematical morphology algorithm (AMM) uses an adaptive structuring element to extract AAs based on their morphological features. The relative-energy algorithm (Rel-En) uses short- and long-term energies to enhance and detect the AAs in the IEGM signals. Second, following the AA extraction, the signal amplitude was weighted using statistics of the AA sequences in order to reduce over- and undersensing of the algorithms. The detection capacity of our algorithms was compared with manually annotated activations and with two previously developed algorithms based on the Teager-Kaiser energy operator and the AF cycle length iteration, respectively. Finally, a method based on the barycenter was developed to reduce artificial variations in the activation annotations of complex IEGM signals. RESULTS: The best detection was achieved using Rel-En, yielding a false negative rate of 0.76% and a false positive rate of only 0.12% (total error rate 0.88%) against expert annotation. The post-processing further reduced the total error rate of the Rel-En algorithm by 70% (yielding to a final total error rate of 0.28%). CONCLUSION: The proposed method shows reliable detection and robust temporal annotation of AAs recorded within pulmonary veins in AF. The method has low computational cost and high robustness for automatic detection of AAs, which makes it a suitable approach for online use in a procedural context.

Atrial fibrillation septal pacing: translation of modelling results.

AIMS: Atrial fibrillation (AF) septal pacing consists of rapid pacing from a ring of electrodes around the atrial septum, leading to local capture of both atria during AF. The present model-based study evaluated the impact of the number of stimulation electrodes in the septal ring on AF capture for different types of sustained AF dynamics. METHODS AND RESULTS: Using a biophysical model of AF based on CT scans from an AF patient, models with different AF substrates (Cholinergic AF and Meandering Wavelets) were created by varying the atrial membrane kinetics. Rapid pacing was applied from the septum area with a ring of 1, 2, 3, 4, 6, 8, or 12 electrodes during 20 seconds at a pacing cycle lengths (PCLs) in the range 60-100% of AF cycle length (AFCL), in 4% steps. Percentage of captured tissue during rapid pacing was determined using 24 sensing electrode pairs evenly distributed on the atrial surface. Results were averaged over 10 AF simulations. For Cholinergic AF, the number of stimulation electrodes on the septal ring had no significant impact on AF capture independently of AF dynamics. For Meandering Wavelets, more electrodes were needed to achieve AF capture in the presence of complex AF. CONCLUSION: Changes in AF substrate significantly impacted septal pacing outcomes and response to rapid AF pacing may similarly vary patient-to-patient. The number of stimulation electrodes had a lesser impact, suggesting that the design of a ring with 3-4 electrodes around the septum would be sufficient for most AF dynamics.