Atrial Fibrillation Waves Research Articles

Abstract 4138724: Artificial-Intelligence Based Tracking of Atrial Fibrillation Waves that Exit Pulmonary Veins Predicts Response to Ablation

Background: Pulmonary vein isolation (PVI) has been shown to reduce morbidity and mortality in patients with atrial fibrillation (AF). However, a major challenge is identify which patients will or will not respond. Hypothesis: We hypothesized that patients in whom AF activity predominantly exits the pulmonary veins (PV), identified using a novel artificial-intelligence (AI) wave tracking approach, will respond to ablation compared to patients in whom activity predominantly enters the PVs. Methods: We examined 26,640 electrograms in N=37 patients at AF ablation (age 66.3 ± 8.6 years, 13.5% females, 59.5% non-paroxysmal AF). Patients had AF recordings with multipolar catheters. We analyzed AF in 3X3 electrode grids, representing 1-3 cm 2 areas that approximate minimum tissue wavelength, near and remote from PVs. For each grid, we determined if AF waves exited PVs (i.e. active PVs) or entered PVs using AI-algorithms to assign electrode activations and grid vectors. We compared the numbers of waves that exited versus entered PVs relative to ablation success, defined using ambulatory ECGs over 1 year. Results: Spatial grids in Fig A show AF waves exiting the left superior PV in a 60 year old man with persistent AF whose PVI was successful. Fig B shows AF waves entering the PVs in a 70 year old woman whose subsequent PVI was unsuccessful. Examining 720 grids per patient (48 four second grids over 1 minute), AF waves varied considerably. Fig. C shows that more AF waves exited the PVs in patients with PVI success than in patients with PVI failure (p=0.03). Conversely, more AF waves headed towards the PVs in patients with PVI failure than with PVI success (p=0.04). Conclusions: In this study, AI-based wave identification enabled the identification of AF patients in whom PVI was more or less successful. The approach to determine active versus passive PVs could be extended to other metrics, including non-invasive tools.

Abstract 4137246: Atrial Fibrillation Wave Vectors Determined by Artificial-Intelligence Indicate Clinical Phenotypes

Background: Atrial fibrillation (AF) is a major cause of morbidity and mortality, which may be reduced by ablation. However, it is difficult to predict patients who will respond, which is currently done using bedside variables since even experts find it difficult to interpret electrical signals (electrograms) or maps in AF, or link them with phenotype, acute or long-term ablation response. Hypothesis: We hypothesized that determining if AF waves consistently propagate away from the pulmonary veins (PV), identified using artificial-intelligence (AI) tools applied to spatial grids in multipolar catheters, may reflect phenotypes of response to ablation or other clinical features. Methods: We studied 26,640 electrograms in N=37 patients at AF ablation ( 66.3 ± 8.6 years, 13.5% women, 59.5% non-paroxysmal AF). We trained AI-algorithms to identify activation patterns and calculate propagation vectors in AF. Each patient had AF recordings from 64-pole basket catheters, from which we calculated AI-based vectors in 48 spatial grids of 3X3 electrodes (covering ~1-3 cm 2 ) over 1 minute (720 per patient). Fig. A illustrates AF waves predominantly exiting the PVs in a 60 year old man. We studied wave maps exiting the PVs in relation to AF type, termination of AF during ablation and 1 year outcome. Results: Ablation success for the cohort was 71.4% at 1 year. Wave direction in AF shifted over time, as expected, but predominant vectors correlated with clinical outcomes. Fig. B showed that AF waves were significantly more likely to exit the PVs for patients with success than failed ablation (<0.05). Fig. C showed that PV wave direction did not differ for AF type, nor for patients with or without acute AF termination by ablation (p=NS). Conclusions: In this study of patients with various types of AF, a novel AI-based approach showed that AF vectors indicating waves exiting the PVs predicted , clinical phenotypes, particularly patients with successful ablation targeting the PVs.

Atypical Presentation of Pre-excited Atrial Fibrillation in an Elderly Patient: A Case Report

Pre-excited atrial fibrillation (AF) is a significant clinical condition associated with Wolff-Parkinson-White (WPW) syndrome, characterized by an accessory pathway that enables rapid conduction of atrial fibrillatory waves to the ventricles, leading to dangerously elevated ventricular rates. While WPW syndrome is typically diagnosed in younger individuals, its occurrence in elderly patients is rare and often underreported. This case report discusses a 79-year-old male with well-controlled hypertension who was admitted for poorly tolerated irregular tachycardia with broad QRS complexes, indicative of pre-excited atrial fibrillation (AF). Urgent synchronized electrical cardioversion successfully restored sinus rhythm. Although catheter ablation was proposed as a definitive treatment, the patient chose to postpone the procedure, with close monitoring implemented afterward. This case underscores the challenges of diagnosing WPW syndrome in elderly patients, the potential dangers of pre-excited AF, and the importance of individualized management strategies tailored to patient preferences and comorbidities.

The feasibility of atrial Fibrillatory wave amplitude in predicting ablation outcomes in persistent atrial fibrillation

BackgroundAtrial fibrosis has a significant impact on the success rate of catheter ablation (CA) treatment of atrial fibrillation (AF). The fibrotic tissues could be reflected by the amplitude of the fibrillatory wave (F-wave). Methods and Results704 patients with persistent AF and at least 1-year follow-up after CA were included as the internal group. 101 patients from another hospital were used as the external validation cohort. A 12‑lead ECG was performed before CA and the maximum FWA in three ECG leads (aVL, aVF, V1) were measured. The FWA score (0 to 6 points according to the amplitude range of the three leads) of each patients was calculated. Five models including clinical features, FWA score, CHA2DS2-VASc score, APPLE score and the fusion of clinical features and FWA score were built. The FWA score was superior to the model constructed by clinical variables, CHA2DS2-VASc score and APPLE score. It not only had good predictive performance for AF recurrence, with an AUC value of 0.812 (95% CI 0.724–0.900), but also showed a significant predictive value for the recurrence rate according to F-wave amplitude. In the external validation cohort, the FWA score showed similar results (AUC 0.768, 95% CI 0.672–0.865). ConclusionsThe present study reveals the significant predictive value of the FWA score for persistent AF ablation recurrence.

The frequency of atrial fibrillatory waves is modulated by the spatiotemporal pattern of acetylcholine release: a 3D computational study.

In atrial fibrillation (AF), the ECG P-wave, which represents atrial depolarization, is replaced with chaotic and irregular fibrillation waves (f waves). The f-wave frequency, F f, shows significant variations over time. Cardiorespiratory interactions regulated by the autonomic nervous system have been suggested to play a role in such variations. We conducted a simulation study to test whether the spatiotemporal release pattern of the parasympathetic neurotransmitter acetylcholine (ACh) modulates the frequency of atrial reentrant circuits. Understanding parasympathetic involvement in AF may guide more effective treatment approaches and could help to design autonomic markers alternative to heart rate variability (HRV), which is not available in AF patients. 2D tissue and 3D whole-atria models of human atrial electrophysiology in persistent AF were built. Different ACh release percentages (8% and 30%) and spatial ACh release patterns, including spatially random release and release from ganglionated plexi (GPs) and associated nerves, were considered. The temporal pattern of ACh release, ACh(t), was simulated following a sinusoidal waveform of frequency 0.125 Hz to represent the respiratory frequency. Different mean concentrations and peak-to-peak ranges of ACh (ΔACh) were tested. We found that temporal variations in F f, F f(t), followed the simulated temporal ACh(t) pattern in all cases. The temporal mean of F f(t), , depended on the fibrillatory pattern (number and location of rotors), the percentage of ACh release nodes and . The magnitude of F f(t) modulation, ΔF f, depended on the percentage of ACh release nodes and ΔACh. The spatial pattern of ACh release did not have an impact on and only a mild impact on ΔF f. The f-wave frequency, being indicative of vagal activity, has the potential to drive autonomic-based therapeutic actions and could replace HRV markers not quantifiable from AF patients.

Atrial fibrillatory wave amplitude revisited: A predictor of recurrence after catheter ablation independent of the degree of left atrial structural remodeling.

The fibrillatory wave amplitude (FWA) during atrial fibrillation (AF) is thought to reflect structural atrial remodeling, but it remains unclear what determines the FWA. 114 consecutive patients were prospectively studied who underwent catheter ablation of AF. The mean FWA was computed by automated surface ECG analyses. The extent of the left atrial (LA) voltage-defined atrial fibrosis and conduction properties were estimated by a three-dimensional high-density electroanatomical mapping system. The LA size was evaluated by transthoracic echocardiography. The study patients were divided into 2 groups according to an FWA in lead V1 above the median value of 46 µV (high FWA group, n=57) or below 46 µV (low FWA group, n=57). There were no differences in the age, gender, CHADS2 score, prevalence of paroxysmal AF, medications, ablation strategy, and LA volume index between the two groups. The LA low voltage areas in the low FWA group were not different from those in the high FWA group. The total LA activation time and local LA conduction velocity did not differ between the two groups. During a median follow-up of 710 days, the recurrence rate after ablation was significantly higher in patients with a low FWA than a high FWA (log-rank P=0.02). In a multivariate analysis, non-paroxysmal AF, the LA volume index, and FWA were independent predictors of recurrence after ablation. The FWA was not correlated with the markers of atrial structural remodeling. Nevertheless, the FWA could still provide information for predicting the clinical outcome after AF ablation.

A comparative study of the performance of methods for f-wave extraction

Objective. This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods. Approach. We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features. Main results. The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Himalayan Fibrillatory Waves in Juvenile Rheumatic Mitral Stenosis

Abstract An 18-year-old girl, a known case of rheumatic mitral stenosis, presented with dyspnea and palpitations. Electrocardiogram was done, which revealed atrial fibrillation and a large amplitude atrial fibrillatory wave, which was more than the voltage of R wave V1, a finding which is quite uncommon. Echocardiography revealed a large left atrium which was 80 mm in diameter. The patient was started on oral anticoagulation and referred for mitral valve replacement with maze procedure.

B-PO02-044 ELECTROGRAM FINGERPRINTS OF ATRIAL FIBRILLATION

It is unclear whether disorganized waves in atrial fibrillation (AF) produce distinct ‘signature shapes’ on the electrogram (EGM), reflecting specific patterns. We tested the hypothesis that deep learning (DL) could be used to identify specific EGM shapes pathognomic for AF, that could form the basis for future approaches to detect subtype and treat patients. We developed custom-built convolutional (CNN) and recurrent (RNN) neural networks using N=29,340 EGM segments (4 sec), from 86 patients with AF or AFL at ablation (25 female, 65±11 years), and using 10-fold cross-validation (80% patients for training, 20% for independent testing). We probed CNN by generating 29,880 EGM sequences from patient data, in which we successively controlled variables including cycle length (CL) and EGM shape. Trained CNN and RNN identified AF from other atrial arrhythmias with an AUC of 0.95 ± 0.05 and 0.97 ± 0.04 in the testing sets respectively (Fig. A). We identified EGM shapes that were classified as AF even if they had regular rhythm and slow rate (long CL). A systematic analysis of 101 such AF signature EGMs were classified as AF >80% by CNN regardless of rate and irregularity (fig B, orange), and also by the RNN on 30 of such EGM signatures. Signature AF electrograms were identical by both RNN and CNN classification for 15 shapes (fig C, red shapes). Deep learning identified signature AF electrograms. EGM signature shapes were more important to AF L classification than high rate or irregular timing. Extraction of complex electrophysiological information hidden to the expert eye may improve diagnosis and shed insights into mechanisms of EGM generation in AF.

B-AB07-04 A SIMPLE AND EASY GUIDE TO PREDICT THE RISK OF ATRIAL FIBRILLATION RECURRENCE POST ABLATION: CHECK OUT THE SIZE OF THE FIBRILLATORY WAVES

The size of fibrillatory waves is a clinical surrogate of structural and electrophysiological characteristics of the atrium. A simple and easy method to predict post ablation atrial fibrillation (AF) recurrence is needed to risk stratify patients prior to ablation. To determine if amplitude of atrial fibrillatory waves can be used to predict recurrence of AF post ablation. We performed a retrospective review of patients who underwent radiofrequency catheter ablation for atrial fibrillation at two institutions. Baseline characteristics, EKG parameters, and AF recurrence at 1 year post ablation were noted. Coarse fibrillatory waves were defined as > 1 mm in Lead II or V1. Fine fibrillatory waves were defined as < 1 mm in Lead II or V1. A total of 120 patients underwent AF ablation. Majority of patients had fine AF (72%) as opposed to coarse AF (28%) in their pre-ablation electrocardiograms. Coarse AF population was older (67±11 vs 62±10 years, p-value 0.053). No gender or racial differences were noted. There were minimal differences in the type of AF present in both groups (Paroxysmal AF 42% vs 46%). Percentage of patients on Class IC agents pre-ablation was not different in both groups (p=0.1). AF recurrence was more common in patients with coarse AF as compared to fine AF (70% vs 32%, p=0.0002) at 1 year. There were no other significant univariate predictors of AF recurrence post ablation. Size of the fibrillatory waves can be used to predict success of AF ablation. While fine AF is more commonly found, coarse AF is associated with significantly higher risk of AF recurrence post ablation.

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f-waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f-wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f-wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f-wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f-wave signals.

ECG-Derived Respiratory Rate in Atrial Fibrillation.

The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated. The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals. Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015 ± 0.021Hz and 0.019 ± 0.025Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034 ± 0.039Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA. The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed. The respiratory rate can be robustly estimated from the ECG in the presence of AF.

Ranking of the most reliable beat morphology and heart rate variability features for the detection of atrial fibrillation in short single-lead ECG

Objective: This study participated in the 2017 PhysioNet/CinC Challenge dedicated to the classification of atrial fibrillation (AF), normal sinus rhythm (Normal), other arrhythmia (Other) and strong noise, using single-lead electrocardiogram (ECG) recordings with a duration <60 s. The aim is to apply a linear threshold-based strategy for arrhythmia classification, ranking the most powerful time domain ECG features that could be easily reproduced on any platform. Approach: An algorithm for time domain ECG analysis was designed to extract 44 features with focus on the following: noise detection; heart rate variability (HRV) analysis; beat morphology analysis and delineation of P-, QRS-, and T-waves in the robust average beat; detection of atrial activity by the presence of P-waves in the average beat and atrial fibrillatory waves (f-waves) during TQ intervals. A linear discriminant analysis (LDA) classifier was optimized on the Challenge training set (8528 ECGs) by stepwise selection of a nonredundant feature set until maximization of the Challenge F1 score. Heart rate (HR) was an independent factor for the LDA classifier design, particular to bradycardia (HR ⩽ 50 bpm), normal rhythm (HR = 50–100 bpm), tachycardia (HR ⩾ 100 bpm). Main results: The algorithm obtained official Challenge F1 scores of 0.80 (Overall), 0.90 (Normal), 0.81 (AF), 0.70 (Other), and 0.54 (Noise) on the hidden Challenge test set (3658 ECGs). This is equivalent to a true positive rate (TPR) = 90.1% (Normal), 81.5% (AF), 67.7% (Other), and 69.5% (Noise), and a false positive rate (FPR) = 13.6% (Normal), 2.3% (AF), 7.7% (Other), and 1.5% (Noise). Significance: The top five features, which together contributed to about 94% of the maximal F1 score were ranked: (1) proportion of RR intervals differing by >50 ms from the preceding RR interval; (2) Poincaré plot geometry estimated by the ratio of the minor-to-major semi-axes of the fitted ellipse; (3) P-wave presence in the average beat; (4) mean percentage of the RR interval first differences; and (5) mean correlation of all beats against the average beat. The global rank of feature extraction methods highlighted that HRV alone was able to provide 92.5% of the maximal F1 score (0.74 versus 0.8). The added value of more complex ECG morphology analysis was less significant for Normal, AF, and Other rhythms (+0.02 to 0.08 points) than for Noise (+0.19 points); however, these were indispensable for wearable ECG recording devices with frequent artefact disturbance.

Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome.

Background: The use of surface recordings to assess atrial fibrillation (AF) complexity is still limited in clinical practice. We propose a noninvasive tool to quantify AF complexity from body surface potential maps (BSPMs) that could be used to choose patients who are eligible for AF ablation and assess therapy impact.Methods: BSPMs (mean duration: 7 ± 4 s) were recorded with a 252-lead vest in 97 persistent AF patients (80 male, 64 ± 11 years, duration 9.6 ± 10.4 months) before undergoing catheter ablation. Baseline cycle length (CL) was measured in the left atrial appendage. The procedural endpoint was AF termination. The ablation strategy impact was defined in terms of number of regions ablated, radiofrequency delivery time to achieve AF termination, and acute outcome. The atrial fibrillatory wave signal extracted from BSPMs was divided in 0.5-s consecutive segments, each projected on a 3D subspace determined through principal component analysis (PCA) in the current frame. We introduced the nondipolar component index (NDI) that quantifies the fraction of energy retained after subtracting an equivalent PCA dipolar approximation of heart electrical activity. AF complexity was assessed by the NDI averaged over the entire recording and compared to ablation strategy.Results: AF terminated in 77 patients (79%), whose baseline AF CL was 177 ± 40 ms, whereas it was 157 ± 26 ms in patients with unsuccessful ablation outcome (p = 0.0586). Mean radiofrequency emission duration was 35 ± 21 min; 4 ± 2 regions were targeted. Long-lasting AF patients (≥12 months) exhibited higher complexity, with higher NDI values (≥12 months: 0.12 ± 0.04 vs. <12 months: 0.09 ± 0.03, p < 0.01) and short CLs (<160 ms: 0.12 ± 0.03 vs. between 160 and 180 ms: 0.10 ± 0.03 vs. >180 ms: 0.09 ± 0.03, p < 0.01). More organized AF as measured by lower NDI was associated with successful ablation outcome (termination: 0.10 ± 0.03 vs. no termination: 0.12 ± 0.04, p < 0.01), shorter procedures (<30 min: 0.09 ± 0.04 vs. ≥30 min: 0.11 ± 0.03, p < 0.001) and fewer ablation targets (<4: 0.09 ± 0.03 vs. ≥4: 0.11 ± 0.04, p < 0.01).Conclusions: AF complexity can be noninvasively quantified by PCA in BSPMs and correlates with ablation outcome and AF pathophysiology.

Electrocardiography-controlled central venous catheter tip positioning in patients with atrial fibrillation.

A significant increase of the p-wave of a real-time intracavitary electrocardiography is a reliable and safe method to confirm the central venous catheter tip position close to the atrium. However, conflicting data about the feasibility of electrocardiography exist in patients with atrial fibrillation. An observational prospective case-control cohort study was set up to study the feasibility and accuracy of the electrocardiography-controlled central venous catheter tip placement in 13 patients with atrial fibrillation versus 10 patients with sinus rhythm scheduled for elective surgery. Each intervention was crosschecked with ultrasound-guided positioning via right supraclavicular fossa view and chest radiography. Ultrasound-guided supraclavicular venipuncture of the right subclavian vein and guidewire advancement were performed. A B-mode view of the superior vena cava and the right pulmonary artery was obtained to visualize the J-tip of the guidewire. The central venous catheter was advanced over the guidewire and the electrocardiography was derived from the J-tip of the guidewire protruding from the central venous catheter tip. Electrocardiography was read for increased p- and atrial fibrillation waves, respectively, and insertion depth was compared with the ultrasound method. Electrocardiography indicated significantly increasing fibrillation and p-waves, respectively, in all patients and ultrasound-guided central venous catheter positioning confirmed a tip position within the lower third of the superior vena cava. Electrocardiography-guided central venous catheter tip positioning is a feasible real-time method for patients with atrial fibrillation. Combined with ultrasound, the electrocardiography-controlled central venous catheter placement may eliminate the need for postinterventional radiation exposure.

P1718Non-paroxysmal atrial fibrillation wave dynamics were determined by age rather than echocardiographic measurements and BNP: A clinical study using the ExTRa Mapping system

P1718Non-paroxysmal atrial fibrillation wave dynamics were determined by age rather than echocardiographic measurements and BNP: A clinical study using the ExTRa Mapping system

An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation.

An extended nonlinear Bayesian filtering framework is introduced for the analysis of atrial fibrillation (AF), in particular with single-channel electrocardiographical (ECG) recordings. It is suitable for simultaneously tracking the fundamental frequency of atrial fibrillatory waves (f-waves), and separating signals, linked to atrial and ventricular activity, during AF. In this framework, high-power ECG components, i.e., Q, R, S, and T waves, are modeled using sum of Gaussian functions. The atrial activity dynamical model is instead based on a trigonometrical function, with a fundamental frequency (the inverse of the dominant atrial cycle length), and its harmonics. The state variables of both dynamical models (QRS-T and f-waves) are hidden and, then estimated, sample by sample, using a Kalman smoother. Remarkably, the scheme is capable of separating ventricular and atrial activity signals, while contemporarily tracking the atrial fundamental frequency in time. The proposed method was evaluated using synthetic signals. In 290 ECGs in sinus rhythm from the PhysioNet PTB Diagnostic ECG Database, the P-waves were replaced with a synthetic f-wave. Broadband noise at different signal-to-noise ratio (SNR) (from 0 to 40 dB) was added to study the performance of the filter, under different SNR conditions. The results of the study demonstrated superior results in atrial and ventricular signal separation when compared with traditional average beat subtraction (ABS), one of the most widely used method for QRS-T cancellation (normalized mean square error = 0.045 for extended Kalman smoother (EKS) and 0.18 for ABS, SNR improvement was 21.1 dB for EKS and 12.2 dB for ABS in f-wave extraction). Various advantages of the proposed method have been addressed and demonstrated, including the problem of tracking the fundamental frequency of f-waves (root mean square error (RMSE) Hz for gradually changing frequency at SNR=15 dB) and of estimating robust QT/RR values during AF (RMSE at SNR=10 dB, ).

Ranolazine reduces atrial fibrillatory wave frequency.

Antiarrhythmic medications for the treatment of atrial fibrillation (AF) have limited efficacy and rare but potentially life-threatening side effects. Ranolazine is an antianginal agent that may have antiarrhythmic activity in AF. Using the Duke Enterprise Data Unified Content Explorer database, we analysed a cohort of AF patients on ranolazine. Patients served as their own historic control. Electrocardiograms (ECGs) were analysed before and after ranolazine initiation to determine the effect of ranolazine on dominant frequency (DF), f-wave amplitude, and organizational index (OI). We identified 15 patients with ECGs in AF before and after ranolazine. Ranolazine was associated with lower DF by an average of 10% (5.10 ± 0.74 vs. 5.79 ± 0.96 Hz, P = 0.04) but not with changes in OI (0.47 ± 0.11 vs. 0.50 ± 0.12, P = 0.71) or amplitude (0.47 ± 0.43 vs. 0.41 ± 0.40 mV, P = 0.82). Ranolazine was also associated with lower DF in patients (n = 10) not on concomitant antiarrhythmic therapy (5.25 ± 0.78 vs. 6.03 ± 0.79 Hz, P = 0.04). Ranolazine is associated with lower AF DF but no change in OI or fibrillatory wave amplitude. Prospective trials are needed to evaluate ranolazine's potential as a novel antiarrhythmic drug for AF.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads

Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures.

53 Coarse Fibrillatory Waves in Atrial Fibrillation Predict Success of Electrical Cardioversion

<h3>Introduction</h3> Electrical cardioversion (ECV) is an established therapy for the management of persistent atrial fibrillation (AF). Although ECV is initially successful, recurrence rates of 70–80% have been reported at follow-up at 1 year.¹ Fibrillatory waves (Fw) can often been seen on the surface ECG in AF and can be characterised simply and reliably based on their amplitude.² Amplitudes of ≥0.1 mV are classified as coarse, whilst &lt;0.1 mV are classified as fine (Figure 1). Recent invasive human electrophysiological studies have suggested that AF initiates via organised mechanisms.³ We hypothesised that coarse Fw reflected more ‘early’ organised AF and were therefore a predictor of maintenance of sinus rhythm (SR) after ECV. <h3>Methods</h3> We conducted a single centre retrospective observation study by obtaining records of 138 consecutive ECVs. Two cardiologists independently analysed the pre-ECV ECG and classified fine (fFw) or coarse (cFw) F-waves in leads II and V1 using callipers. Follow up was by analysing the 4-week post ECV ECG to assess rhythm. Demographics, drug treatments and echocardiography parameters were analysed as well. <h3>Results</h3> From the 138 consecutive ECVs, 8 were excluded due to incomplete datasets, 15 due to atrial flutter being the starting rhythm and 3 due to non-concurrent classification of the Fw by the two cardiologists. Out of the 112 pre-ECV ECGs, 32% (36/112) had cFw in V1, lead II or both. After ECV, SR was immediately achieved in 95% (106/112) of patients. Overall, of those that were successful, 46% (49/106) were back in AF at the 4-week clinic visit. All patients with cFw had successful initial ECV. At follow-up, 75% (27/36) of patients with cFw on pre-ECV remained in SR compared with 43% (30/70) of patients with fFw who had a successful initial ECV (p = 0.001, Fisher’s exact test). The cFw and fFw groups were of the similar mean age (70.5 vs. 71.2, p = 0.72). Amiodarone use was similar between groups (8% vs. 17%, p = 0.26) as was left atrial size (4.2 vs. 4.4 cm, p = 0.3); left atrial volume (59 vs. 54 mls, p = 0.5) and indexed left atrial volume (41.8 vs. 39.0 mls/m², p = 0.34) (Table 1). <h3>Conclusion</h3> The presence of coarse Fw in V1, lead II or both resulted in a significantly higher ECV success rate at 4 weeks than fine Fw in both leads. This appeared to be independent of traditional factors thought to affect success. The presence of cFw may be a simple ECG based measure to refine the role of ECV in the treatment of patients with AF. <h3>References</h3> Lundström T, Rydén L. Chronic atrial fibrillation. Long-term results of direct current conversion. ActaMedScand.1988;223(1):53–59 Xi Q, Swiryn S. Atrial fibrillatory wave characteristics on surface electrogram. J Cardiovasc Electrophysiol. 2004;15(8):911–917 Schricker AA, Narayan SM. Human Atrial Fibrillation Initiates via Organized Rather Than Disorganized Mechanisms. Circ Arrhythm Electrophysiol. 2014;7(5):816–824