Assessment of global atrial fibrillation organization to optimize timing of atrial defibrillation

Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue

This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.

High-resolution spatiotemporal changes in dominant frequency and structural organization during persistent atrial fibrillation

OBJECTIVE

Analyze changes in frequency activity and structural organization that occur over time with persistent atrial fibrillation (AF).

BACKGROUND

Little is known about the frequency characteristics of the epicardium during transition from paroxysmal to persistent AF. Accurate identification of areas of high dominant frequency (DF) is often hampered by limited spatial resolution. Improvements in electrode arrays provide high spatiotemporal resolution, allowing for characterization of the changes that occur during this transition.

METHODS

AF was induced in adult Yorkshire swine by atrial tachypacing. DF mapping was performed using personalized mapping arrays. Histological analysis and late gadolinium enhanced magnetic resonance imaging were performed to determine structural differences in fibrosis.

RESULTS

The left atrial epicardium was associated with a significant increase in DF in persistent AF (6.5 ± 0.2 vs. 7.4 ± 0.5 Hz, P = 0.03). 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). MRI analysis demonstrated increased ECV values in persistent AF (0.19 vs 0.34, paroxysmal vs persistent, P = 0.05). Tissue sections from the atria showed increase in fibrosis in pigs with persistent AF compared to paroxysmal AF. Staining demonstrated decreased myocardial fiber alignment and loss of anisotropy in persistent AF tissue.

CONCLUSIONS

Changes in tissue organization and fibrosis are observed in the porcine model of persistent AF. Alterations in frequency activity and organization index can be captured with high resolution using flexible electrode arrays.

An Evaluation of Phase Analysis to Interpret Atrial Activation Patterns during Persistent Atrial Fibrillation for Targeted Ablation

BACKGROUND

Phase analysis has been used to identify and localize atrial fibrillation (AF) sources for targeted ablation. We previously demonstrated that repetitive wannabe reentry (incomplete reentry) often generated an apparent stable rotor using phase analysis. The misinterpretation caused by phase analysis using atrial electrograms (AEGs) may result from detecting inaccurate time points at phase inversion (π to -π) in the instantaneous phase waveform converted from AEG. The purpose of this study was to evaluate the accuracy of phase analysis to detect atrial activations recorded from the high-density mapping of AF in patients with persistent and long-standing persistent (LSP) AF.

METHODS AND RESULTS

During open heart surgery, we recorded activation from both atria simultaneously using 512 electrodes in 7 patients with persistent and LSP AF. The phase analysis was compared to manual measurements during 4 s of data. For the accuracy of activation sequence maps, a successful recording site was defined as having ≤4 mismatched activation times during the 4 s. In all AF episodes, the accuracy of the phase analysis was only 82% of the total number of activation times due to either activation time differences (14.7%), under-sensing (2.7%), or over-sensing (0.6%). Only 67.9% of the total recording sites met the requirement of a successful recording site by phase analysis. In unsuccessful recording sites, AEG characteristics were relatively irregular cycle length (CL), complex AEG, and double potential AEG.

CONCLUSION

The phase analysis was less accurate in recording sites with a relatively irregular CL, complex AEG, or double potential AEG. As a result, phase analysis may lead to the misinterpretation of atrial activation patterns during AF. A visual review of the original AEG is needed to confirm the detected AF sources of phase analysis before performing targeted ablation.

Machine learning based potentiating impacts of 12-lead ECG for classifying paroxysmal versus non-paroxysmal atrial fibrillation

Background

Conventional modality requires several days observation by Holter monitor to differentiate atrial fibrillation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed.

Objective

To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF.

Methods

In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model.

Results

In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteristics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set.

Conclusions

Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model performance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

Attenuation of Oxidative Injury With Targeted Expression of NADPH Oxidase 2 Short Hairpin RNA Prevents Onset and Maintenance of Electrical Remodeling in the Canine Atrium: A Novel Gene Therapy Approach to Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is the most common heart rhythm disorder in adults and a major cause of stroke. Unfortunately, current treatments of AF are suboptimal because they are not targeted to the molecular mechanisms underlying AF. Using a highly novel gene therapy approach in a canine, rapid atrial pacing model of AF, we demonstrate that NADPH oxidase 2 (NOX2) generated oxidative injury causes upregulation of a constitutively active form of acetylcholine-dependent K+ current (IKACh), called IKH; this is an important mechanism underlying not only the genesis, but also the perpetuation of electric remodeling in the intact, fibrillating atrium.

METHODS

To understand the mechanism by which oxidative injury promotes the genesis and maintenance of AF, we performed targeted injection of NOX2 short hairpin RNA (followed by electroporation to facilitate gene delivery) in atria of healthy dogs followed by rapid atrial pacing. We used in vivo high-density electric mapping, isolation of atrial myocytes, whole-cell patch clamping, in vitro tachypacing of atrial myocytes, lucigenin chemiluminescence assay, immunoblotting, real-time polymerase chain reaction, immunohistochemistry, and Masson trichrome staining.

RESULTS

First, we demonstrate that generation of oxidative injury in atrial myocytes is a frequency-dependent process, with rapid pacing in canine atrial myocytes inducing oxidative injury through the induction of NOX2 and the generation of mitochondrial reactive oxygen species. We show that oxidative injury likely contributes to electric remodeling in AF by upregulating IKACh by a mechanism involving frequency-dependent activation of PKCε (protein kinase C epsilon). The time to onset of nonsustained AF increased by >5-fold in NOX2 short hairpin RNA-treated dogs. Furthermore, animals treated with NOX2 short hairpin RNA did not develop sustained AF for up to 12 weeks. The electrophysiological mechanism underlying AF prevention was prolongation of atrial effective refractory periods, at least in part attributable to the attenuation of IKACh. Attenuated membrane translocation of PKCε appeared to be a likely molecular mechanism underlying this beneficial electrophysiological remodeling.

CONCLUSIONS

NOX2 oxidative injury (1) underlies the onset, and the maintenance of electric remodeling in AF, as well, and (2) can be successfully prevented with a novel, gene-based approach. Future optimization of this approach may lead to a novel, mechanism-guided therapy for AF.

Unsupervised Classification of Atrial Electrograms for Electroanatomic Mapping of Human Persistent Atrial Fibrillation

OBJECTIVE

Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a 'ground truth' for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers.

METHODS

956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS).

RESULTS

Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization.

CONCLUSIONS

Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies.

Characterization of atrial arrhythmias in body surface potential mapping: A computational study

PURPOSE

Atrial tachycardia (AT), flutter (AFL) and fibrillation (AF) are very common cardiac arrhythmias and are driven by localized sources that can be ablation targets. Non-invasive body surface potential mapping (BSPM) can be useful for early diagnosis and ablation planning. We aimed to characterize and differentiate the arrhythmic mechanisms behind AT, AFL and AF from the BSPM perspective using basic features reflecting their electrophysiology.

METHODS

19 simulations of 567-lead BSPMs were used to obtain dominant frequency (DF) maps and estimate the atrial driving frequencies using the highest DF (HDF). Regions with |DF-HDF|≤1Hz were segmented and characterized (size, area); the spatial distribution of the differences |DF-atrialHDFestimate| was qualitatively analyzed. Phase singularity points (SPs) were detected on maps generated with Hilbert transform after band-pass filtering around the HDF (±1Hz). Connected SPs along time (filaments) and their histogram (heatmaps) were used for rotational activity characterization (duration, spatiotemporal stability). Results were reproduced in clinical layouts (252 to 12 leads) and with different rotations and translations of the atria within the torso, and compared with the original 567-lead outcomes using structural similarity index (SSIM) between maps, sensitivity and precision in SP detection and direct feature comparison. Random forest and least-square based algorithms were used to classify the arrhythmias and their mechanisms' location, respectively, based on the obtained features.

RESULTS

Frequency and phase analyses revealed distinct behavior between arrhythmias. AT and AFL presented uniform DF maps with low variance, while AF maps were more heterogeneous. Lower differences from the atrial HDF regions correlated with the driver location. Rotational activity was most stable in AFL, followed by AT and AF. Features were robust to lower spatial resolution layouts and modifications in the atrial geometry; DF and heatmaps presented decreasing SSIM along the layouts. The classification of the arrhythmias and their mechanisms' location achieved balanced accuracy of 72.0% and 73.9%, respectively.

CONCLUSION

Non-invasive characterization of AT, AFL and AF based on realistic models highlights intrinsic differences between the arrhythmias, enhancing the BSPM utility as an auxiliary clinical tool.

Region-specific parasympathetic nerve remodeling in the left atrium contributes to creation of a vulnerable substrate for atrial fibrillation

Atrial fibrillation (AF) is the most common heart rhythm disorder and a major cause of stroke. Unfortunately, current therapies for AF are suboptimal, largely because the molecular mechanisms underlying AF are poorly understood. Since the autonomic nervous system is thought to increase vulnerability to AF, we used a rapid atrial pacing (RAP) canine model to investigate the anatomic and electrophysiological characteristics of autonomic remodeling in different regions of the left atrium. RAP led to marked hypertrophy of parent nerve bundles in the posterior left atrium (PLA), resulting in a global increase in parasympathetic and sympathetic innervation throughout the left atrium. Parasympathetic fibers were more heterogeneously distributed in the PLA when compared with other left atrial regions; this led to greater fractionation and disorganization of AF electrograms in the PLA. Computational modeling revealed that heterogeneously distributed parasympathetic activity exacerbates sympathetic substrate for wave break and reentry. We further discovered that levels of nerve growth factor (NGF) were greatest in the left atrial appendage (LAA), where AF was most organized. Preferential NGF release by the LAA - likely a direct function of frequency and regularity of atrial stimulation - may have important implications for creation of a vulnerable AF substrate.

Systematic differences of non-invasive dominant frequency estimation compared to invasive dominant frequency estimation in atrial fibrillation

Non-invasive analysis of atrial fibrillation (AF) using body surface mapping (BSM) has gained significant interest, with attempts at interpreting atrial spectro-temporal parameters from body surface signals. As these body surface signals could be affected by properties of the torso volume conductor, this interpretation is not always straightforward. This paper highlights the volume conductor effects and influences of the algorithm parameters for identifying the dominant frequency (DF) from cardiac signals collected simultaneously on the torso and atrial surface. Bi-atrial virtual electrograms (VEGMs) and BSMs were recorded simultaneously for 5 min from 10 patients undergoing ablation for persistent AF. Frequency analysis was performed on 4 s segments. DF was defined as the frequency with highest power between 4 and 10 Hz with and without applying organization index (OI) thresholds. The volume conductor effect was assessed by analyzing the highest DF (HDF) difference of each VEGM HDF against its BSM counterpart. Significant differences in HDF values between intra-cardiac and torso signals could be observed, independent of OI threshold. This difference increases with increasing endocardial HDF (BSM-VEGM median difference from -0.13 Hz for VEGM HDF at 6.25 ± 0.25 Hz to -4.24 Hz at 9.75 ± 0.25 Hz), thereby confirming the theory of the volume conductor effect in real-life situations. Applying an OI threshold strongly affected the BSM HDF area size and location and atrial HDF area location. These results suggest that volume conductor and measurement algorithm effects must be considered for appropriate clinical interpretation.

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively. Graphical Abstract Atrial fibrillation detection using PPG signals.

Surface ECG and intracardiac spectral measures predict atrial fibrillation recurrence after catheter ablation

INTRODUCTION

Outcome of patients undergoing catheter ablation of atrial fibrillation (AF) varies widely. We sought to investigate whether parameters derived from the spectral analysis of surface ECG and intracardiac AF electrograms can predict outcome in patients referred for pulmonary vein isolation (PVI).

METHODS

We performed spectral analysis on the surface ECG and intracardiac electrograms from patients referred for AF ablation. After filtering and QRST subtraction, we measured the dominant frequency (DF), regularity index (RI) and the organizational index (OI) of fibrillatory electrograms and determined their value for predicting AF recurrence after ablation. A subjective, blinded prediction based on the surface ECG was also performed.

RESULTS

We analyzed data from 153 PVI procedures in 140 patients (67.1% with persistent or longstanding AF). In a multivariable model, DF in the right atrium (RA) and distal coronary sinus (CSd)-to-RA DF gradient predicted AF recurrence (OR, 3.52, P = 0.023 and OR, 0.2, P = 0.034, respectively). DF in RA and CSd to RA DF gradient had a good predictive value for PVI outcome (area under the curve [AUC] of 0.73, P = 0.007 and 0.74, P = 0.007, respectively). These performed better than the subjective predictions of experienced electrophysiologists ( P = 0.2).

CONCLUSIONS

Higher RA DF, lower CSd to RA DF gradient predicted recurrence after AF ablation. These spectral measures suggest a more remodeled atrial substrate and may provide simple tools for risk stratification or predict the need for additional substrate modification in patients referred for AF ablation.

Ranolazine reduces atrial fibrillatory wave frequency

Aims

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.

Methods and results

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).

Conclusion

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.

Atrial Electrogram Fractionation Distribution before and after Pulmonary Vein Isolation in Human Persistent Atrial Fibrillation-A Retrospective Multivariate Statistical Analysis

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model.

Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs.

Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination (P < 0.0001).

Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

A High Baseline Electrographic Organization Level Is Predictive of Successful Termination of Persistent Atrial Fibrillation by Catheter Ablation

OBJECTIVES

This study sought to investigate whether the level of organization of electrocardiographic (ECG) signals based on novel indexes is predictive of persistent atrial fibrillation (pAF) termination by catheter ablation (CA).

BACKGROUND

Whether the level of ECG organization in pAF is correlated with the restoration of sinus rhythm by CA remains unknown.

METHODS

Thirty consecutive patients who underwent stepwise CA for pAF (sustained duration 19 ± 11 months) were included in the study (derivation cohort). ECG lead V₆ was placed on the patients' back (V_6b) to improve left atrial (LA) recording. Two novel ECG indexes were computed using an adaptive harmonic frequency tracking scheme: 1) the adaptive organization index (AOI), which quantifies the cyclicity of AF harmonic oscillations; and 2) the adaptive phase index (API), which quantifies the phase coupling between the harmonic components. Index cutoff values predictive of procedural AF termination were then tested on a validation cohort of 8 consecutive patients.

RESULTS

In the derivation cohort, CA terminated AF in 21 patients within the LA (70%; left-terminated [LT] group), whereas CA did not terminate AF in 9 patients (30%; non-left-terminated [NLT] group). LT patients displayed a higher ECG organization level at baseline than the NLT patients, with the best separation achieved by AOI and API computed on lead V₁ (area under the curve [AUC] = 0.94 and AUC = 0.88, respectively; p < 0.05) and API on lead V_6b (AUC = 0.83; p < 0.05). Similar results were obtained for both AOI and API in the validation cohort.

CONCLUSIONS

Patients in whom pAF terminated within the LA exhibited a higher level of atrial ECG organization, which was suggestive of a limited number of LA drivers than that of patients in whom the pAF could not be terminated by CA.

A software platform for the comparative analysis of electroanatomic and imaging data including conduction velocity mapping

Electroanatomic mapping systems collect increasingly large quantities of spatially-distributed electrical data which may be potentially further scrutinized post-operatively to expose mechanistic properties which sustain and perpetuate atrial fibrillation. We describe a modular software platform, developed to post-process and rapidly analyse data exported from electroanatomic mapping systems using a range of existing and novel algorithms. Imaging data highlighting regions of scar can also be overlaid for comparison. In particular, we describe the conduction velocity (CV) mapping algorithm used to highlight wavefront behaviour. CV was found to be particularly sensitive to the spatial distribution of the triangulation points and corresponding activation times. A set of geometric conditions were devised for selecting suitable triangulations of the electrogram set for generating CV maps.

Ability of magnetocardiography to detect regional dominant frequencies of atrial fibrillation

BACKGROUND

Lead V1 on electrocardiography (ECG) can detect the dominant frequency (DF) of atrial fibrillation (AF) in the right atrium (RA). Paroxysmal AF is characterized by a frequency gradient from the left atrium (LA) to the right atrium (RA). We examined the ability of magnetocardiography (MCG) to detect regional DFs in both the atria.

METHODS

Study subjects comprised 18 consecutive patients referred for catheter ablation of persistent AF. An MCG system with 64 magnetic sensors was used to perform MCG in the frontal, lateral, and back planes prior to the ablation procedure in each patient. DFMCG and organization index (OIMCG) were calculated using fast Fourier transformation. Intracardiac electrograms (ICEs) in both the atria and the coronary sinus (CS) were mapped at 17 sites. Regional DFsICE were also determined.

RESULTS

Mean LA DFICE was higher than mean RA DFICE (6.40±0.66 versus 6.16±0.80 Hz, P=0.03). DFMCG in the channel having the highest OIMCG was 6.61±0.88 Hz in the frontal plane, 6.52±0.64 Hz in the lateral plane, and 6.42±0.62 Hz in the back plane (P=0.3). In each plane, DFMCG correlated with DFICE at the RA appendage (R=0.95, P<0.0001), the LA appendage (R=0.91, P<0.0001), and the CS (R=0.93, P<0.0001). DFECG in V5 modestly correlated with DFICE at the LA appendage (R=0.82, P<0.0001).

CONCLUSIONS

MCG could more precisely detect the DFs in the LA and the CS than ECG. However, the usefulness of pre-procedural detection of the AF frequency gradient for ablation therapy needs to be evaluated in future prospective studies.

High Atrial Defibrillation Threshold With Internal Cardioversion Indicates Arrhythmogenicity of Superior Vena Cava in Non-Long-Standing Persistent Atrial Fibrillation

BACKGROUND

The purpose of this study was to clarify the relation between atrial defibrillation threshold (ADFT) for internal cardioversion (IC) and arrhythmogenicity of the superior vena cava (SVC).

METHODS AND RESULTS

A total of 159 consecutive patients (139 male, age 59.9±10.3 years) who underwent radiofrequency catheter ablation of atrial fibrillation (AF) were assessed. IC was performed in 50 patients with non-long-standing persistent AF (non-LSAF) with a purpose-built cardioversion catheter in which direct current is delivered between the right atrium and the coronary sinus. SVC arrhythmogenicity was defined as SVC firing initiating AF, SVC associated with maintenance of AF, or frequent ectopy in the SVC. In all 50 non-LSAF patients, AF termination was obtained on IC during the procedure except in 1 patient with SVC AF. In the patients with ADFT >10 J (n=10), SVC arrhythmogenicity was observed more often than in those with ADFT ≤10 J (n=40; 60% vs. 13%; P=0.004). There were no significant differences between the 2 groups in left atrial diameter (40.8±7.6 vs. 40.6±6.3 mm; P=0.92), persistent AF (33% vs. 50%; P=0.46), or other clinical parameters. The patients who underwent SVC isolation, however, had higher ADFT before SVC isolation than those who did not (15.5±8.8 vs. 9.2±4.4 J; P=0.01).

CONCLUSIONS

High IC ADFT is associated with SVC arrhythmogenicity in non-LSAF patients.

Software algorithm and hardware design for real-time implementation of new spectral estimator

Background

Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT).

Method

Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board.

Results

The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2.

Conclusions

The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time.

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned.

Temporal stability in the spectral representation of complex fractionated atrial electrograms

BACKGROUND

Although local electrograms during atrial fibrillation (AF) are often spectrally analyzed over 8-second (8s) intervals, changes may be common over intervals as short as 2s. We sought to determine whether averaged 2s measurements of electrogram spectral parameters were similar to 8s measurements, and whether the 2s intervals could provide an estimate of the temporal stability of the signal frequency content in paroxysmal versus persistent AF.

METHODS

Complex fractionated atrial electrograms (CFAEs) were acquired outside the pulmonary vein ostia and from free wall sites in nine paroxysmal and 10 longstanding persistent AF patients. Using a 2s sliding calculation window, a frequency spectrum was computed every 100 ms over an interval of 8.4 seconds (82 spectra in total). The dominant frequency (DF), the dominant amplitude (DA), and the mean spectral profile (MP) were measured. The 2s measurements were compared to single 8.4-second interval measurements. Coefficients of variation (COV) were computed from the 82 spectra for each CFAE recording to determine temporal variability of parameters.

RESULTS

Over the sliding 2s computation intervals, as for fixed 8.4-second computation intervals, mean DA and DF were significantly higher in longstanding persistent AF while MP was significantly higher in paroxysmal AF (P ≤ 0.001). The COV was significantly higher for the DF parameter in paroxysmal AF (P < 0.001) and significantly higher for the MP parameter in persistent AF (P < 0.02).

CONCLUSIONS

For both paroxysmal and persistent AF data, the 2s sliding window averages provide similar results to single 8.4-second intervals, and information regarding temporal stability was additionally obtained in the process.

Comparison of spectral estimators for characterizing fractionated atrial electrograms

Background

Complex fractionated atrial electrograms (CFAE) acquired during atrial fibrillation (AF) are commonly assessed using the discrete Fourier transform (DFT), but this can lead to inaccuracy. In this study, spectral estimators derived by averaging the autocorrelation function at lags were compared to the DFT.

Method

Bipolar CFAE of at least 16 s duration were obtained from pulmonary vein ostia and left atrial free wall sites (9 paroxysmal and 10 persistent AF patients). Power spectra were computed using the DFT and three other methods: 1. a novel spectral estimator based on signal averaging (NSE), 2. the NSE with harmonic removal (NSH), and 3. the autocorrelation function average at lags (AFA). Three spectral parameters were calculated: 1. the largest fundamental spectral peak, known as the dominant frequency (DF), 2. the DF amplitude (DA), and 3. the mean spectral profile (MP), which quantifies noise floor level. For each spectral estimator and parameter, the significance of the difference between paroxysmal and persistent AF was determined.

Results

For all estimators, mean DA and mean DF values were higher in persistent AF, while the mean MP value was higher in paroxysmal AF. The differences in means between paroxysmals and persistents were highly significant for 3/3 NSE and NSH measurements and for 2/3 DFT and AFA measurements (p<0.001). For all estimators, the standard deviation in DA and MP values were higher in persistent AF, while the standard deviation in DF value was higher in paroxysmal AF. Differences in standard deviations between paroxysmals and persistents were highly significant in 2/3 NSE and NSH measurements, in 1/3 AFA measurements, and in 0/3 DFT measurements.

Conclusions

Measurements made from all four spectral estimators were in agreement as to whether the means and standard deviations in three spectral parameters were greater in CFAEs acquired from paroxysmal or in persistent AF patients. Since the measurements were consistent, use of two or more of these estimators for power spectral analysis can be assistive to evaluate CFAE more objectively and accurately, which may lead to improved clinical outcome. Since the most significant differences overall were achieved using the NSE and NSH estimators, parameters measured from their spectra will likely be the most useful for detecting and discerning electrophysiologic differences in the AF substrate based upon frequency analysis of CFAE.

Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation

BACKGROUND

Successful termination of atrial fibrillation (AF) during catheter ablation (CA) is associated with arrhythmia-free follow-up. Preablation factors such as mean atrial fibrillation cycle length (AFCL) predict the likelihood of AF termination during ablation but recurring patterns and AFCL stability have not been evaluated.

OBJECTIVE

To investigate novel predictors of acute and postoperative ablation outcomes from intracardiac electrograms: (1) recurring AFCL patterns and (2) localization index (LI) of the instantaneous fibrillatory rate distribution.

METHODS

Sixty-two patients with AF (32 paroxysmal AF; 45 men; age 57 ± 10 years) referred for CA were enrolled. One-minute electrogram was recorded from coronary sinus (CS; 5 bipoles) and right atrial appendage (HRA; 2 bipoles). Atrial activations were detected automatically to derive the AFCL and instantaneous fibrillatory rate (inverse of AFCL) time series. Recurring AFCL patterns were quantified by using recurrence plot indices (RPIs): percentage determinism, entropy of determinism, and maximum diagonal length. AFCL stability was determined by using the LI. The CA outcome predictivity of individual indices was assessed.

RESULTS

Patients with terminated atrial fibrillation (T-AF) had higher RPI (P < .05 in CS7-8) and LI than did those with nonterminated atrial fibrillation (P < .005 in CS3-4; P < .05 in CS5-6, CS7-8, and HRA). Patients free of arrhythmia after 3-month follow-up had higher RPI and LI (all P < .05 in CS7-8). All indices except percentage determinism predicted T-AF in CS7-8 (area under the curve [AUC] ≥ 0.71; odds ratio [OR] ≥ 4.50; P < .05). The median AFCL and LI predicted T-AF in HRAD (AUC ≥ 0.75; OR ≥ 7.76; P < .05). The RPI and LI predicted 3-month follow-up in CS7-8 (AUC ≥ 0.68; OR ≥ 4.17; P < .05).

CONCLUSIONS

AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.

Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation

AIMS

This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG).

METHODS AND RESULTS

Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG.

CONCLUSION

Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.

Evolution of activation patterns during long-duration ventricular fibrillation in pigs

Quantitative analysis has demonstrated five temporal stages of activation during the first 10 min of ventricular fibrillation (VF) in dogs. To determine whether these stages exist in another species, we applied the same analysis to the first 10 min of VF recorded in vivo from two 504-electrode arrays, one each on left anterior and posterior ventricular epicardium in six anesthetized pigs. The following descriptors were continuously quantified: 1) number of wavefronts, 2) wavefront fractionations, 3) wavefront collisions, 4) repeatability, 5) multiplicity index, 6) wavefront conduction velocity, 7) activation rate, 8) mean area activated by the wavefronts, 9) negative peak rate of voltage change, 10) incidence of breakthrough/foci, 11) incidence of block, and 12) incidence of reentry. Cluster analysis of these descriptors divided VF into four stages (stages i-iv). The values of most descriptors increased during stage i (1-22 s after VF induction), changed quickly to values indicating greater organization during stage ii (23-39 s), decreased steadily during stage iii (40-187 s), and remained relatively unchanged during stage iv (188-600 s). The epicardium still activated during stage iv instead of becoming silent as in dogs. In conclusion, during the first 10 min, VF activation can be divided into four stages in pigs instead of five stages as in dogs. Following a 16-s period during the first minute of VF when activation became more organized, all parameters exhibited progressive decreased organization. Further studies are warranted to determine whether these changes, particularly the increased organization of stage ii, have clinical consequences, such as alteration in defibrillation efficacy.

Analysis of coronary angiography related psychophysiological responses

BACKGROUND

Coronary angiography is an important tool in diagnosis of cardiovascular diseases. However, it is the administration is relatively stressful and emotionally traumatic for the subjects. The aim of this study is to evaluate psychophysiological responses induced by the coronary angiography instead of subjective methods such as a questionnaire. We have also evaluated the influence of the tranquilizer on the psychophysiological responses.

METHODS

Electrocardiography (ECG), Blood Volume Pulse (BVP), and Galvanic Skin Response (GSR) of 34 patients who underwent coronary angiography operation were recorded. Recordings were done at three phases: "1 hour before," "during," and "1 hour after" the coronary angiography test. Total of 5 features obtained from the physiological signals were compared across these three phases. Sixteen of the patients were administered 5 mg of a tranquilizer (Diazepam) before the operation and remaining 18 were not.

RESULTS

Our results indicate that there is a strong correlation between features (LF/HF, Bk, DN1/DN2, skin conductance level and seg_mean) in terms of reflecting psychophysiological responses. However only DN1/DN2 feature has statistically significant differences between angiography phases (for diazepam: p = 0.0201, for non_diazepam p = 0.0224). We also note that there are statistically significant differences between the diazepam and non-diazepam groups for seg_mean features in "before", "during" and "after" phases (p = 0.0156, 0.0282, and 0.0443, respectively).

CONCLUSIONS

The most intense sympathetic activity is observed in the "during" angiography phase for both of the groups. The obtained features can be used in some clinical studies where generation of the customized/individual diagnoses styles and quantitative evaluation of psychophysiological responses is necessary.

Early temporal and spatial regularization of persistent atrial fibrillation predicts termination and arrhythmia-free outcome

BACKGROUND

Termination of persistent atrial fibrillation (AF) is a valuable ablation endpoint but is difficult to anticipate. We evaluated whether temporal and spatial indices of AF regularization predict intraprocedural AF termination and outcome.

OBJECTIVE

The purpose of this study was to test whether temporospatial organization of AF after pulmonary vein isolation (PVI) predicts whether subsequent stepwise ablation will terminate persistent AF or predict outcome.

METHODS

In 75 patients with persistent AF, we measured AF cycle length (AFCL), temporal regularity index (TRI, a spectral measure of timing regularity), and spatial regularity index (SRI, cycle-to-cycle variations in spatial vector) between right atrial appendage and proximal and distal coronary sinus before and during stepwise ablation to the endpoint of AF termination.

RESULTS

AF termination was achieved in 59 patients (79%) by ablation. AF terminated during PVI in 11 patients, who were excluded from analysis. In the remaining 48 patients, TRI and SRI increased during stepwise ablation, as compared with 16 patients without termination (P<.05). AFCL was prolonged in both groups. From receiver operating characteristics analysis of the first 22 patients (training set), a post-PVI TRI increase predicted AF termination in the latter 42 patients (test set) with a positive predictive value of 96%, negative predictive value of 53%, sensitivity of 71%, and specificity of 91%. Results were similar for SRI. After 36 months, higher arrhythmia-free outcome was observed in patients in whom PVI caused temporospatial regularization in AF.

CONCLUSIONS

Temporal and spatial regularization of persistent AF after PVI identifies patients in whom stepwise ablation subsequently terminates AF and prevents recurrence.

Differences in repeating patterns of complex fractionated left atrial electrograms in longstanding persistent atrial fibrillation as compared with paroxysmal atrial fibrillation

BACKGROUND

Complex fractionated atrial electrograms (CFAE) are morphologically more uniform in persistent longstanding as compared with paroxysmal atrial fibrillation (AF). It was hypothesized that this may result from a greater degree of repetitiveness in CFAE patterns at disparate left atrial (LA) sites in longstanding AF.

METHODS AND RESULTS

CFAEs were obtained from recording sites outside the 4 pulmonary vein (PV) ostia and at a posterior and an anterior LA site during paroxysmal and longstanding persistent AF (10 patients each, 120 sequences total). To quantify repetitiveness in CFAE, the dominant frequency was measured from ensemble spectra using 8.4-second sequences, and repetitiveness was calculated by 2 novel techniques: linear prediction and Fourier reconstruction methods. Lower prediction and reconstruction errors were considered indicative of increasing repetitiveness and decreasing randomness. In patients with paroxysmal AF, CFAE pattern repetitiveness was significantly lower (randomness higher) at antral sites outside PV ostia as compared with LA free wall sites (P < 0.001). In longstanding AF, repetitiveness increased outside the PV ostia, especially outside the left superior PV ostium, and diminished at the LA free wall sites. The result was that in persistent AF, there were no significant site-specific differences in CFAE repetitiveness at the selected LA locations used in this study. Average dominant frequency magnitude was 5.32 ± 0.29 Hz in paroxysmal AF and higher in longstanding AF, at 6.27 ± 0.13 Hz (P < 0.001), with the frequency of local activation approaching a common upper bound for all sites.

CONCLUSIONS

In paroxysmal AF, CFAE repetitiveness is low and randomness high outside the PVs, particularly the left superior PV. As evolution to persistent longstanding AF occurs, CFAE repetitiveness becomes more uniformly distributed at disparate sites, possibly signifying an increasing number of drivers from remote PVs.

Effects of lead spatial resolution on the spectrum of cardiac signals: a simulation study

Spectral analysis is widely applied to bioelectric cardiac signals for quantifying the spatiotemporal organization of cardiac tissue. Nevertheless, to date it is not well understood how lead characteristics affect the spectrum of recorded cardiac signals and, as a consequence, the interpretation of cardiac spectrum is still controversial. In this paper we use simulation methods to investigate the effects of lead spatial resolution on the spectrum of cardiac signals. We simulate three cardiac rhythms of different degrees of spatiotemporal organization in a square sample of cardiac tissue. Then, by using a lead field approach, we synthesize the signals recorded by four idealized leads of different spatial resolution. Finally, we estimate the spectrum of simulated cardiac signals. Our simulations indicate that lead spatial resolution affects cardiac spectrum, although the effects depend on the organization of the underlying rhythm. Specifically, our simulations show that for highly organized rhythms, the smaller the lead resolution region, the broader the distribution of power in frequency. Since lead resolution can affect significantly cardiac spectrum, we conclude that caution should be used when quantifying cardiac spatiotemporal organization based on the spectrum of cardiac signals.

Classifying fractionated electrograms in human atrial fibrillation using monophasic action potentials and activation mapping: evidence for localized drivers, rate acceleration, and nonlocal signal etiologies

BACKGROUND

Complex fractionated electrograms (CFAEs) detected during substrate mapping for atrial fibrillation (AF) reflect etiologies that are difficult to separate. Without knowledge of local refractoriness and activation sequence, CFAEs may represent rapid localized activity, disorganized wave collisions, or far-field electrograms.

OBJECTIVE

The purpose of this study was to separate CFAE types in human AF, using monophasic action potentials (MAPs) to map local refractoriness in AF and multipolar catheters to map activation sequence.

METHODS

MAP and adjacent activation sequences at 124 biatrial sites were studied in 18 patients prior to AF ablation (age 57 ± 13 years, left atrial diameter 45 ± 8 mm). AF cycle length, bipolar voltage, and spectral dominant frequency were measured to characterize types of CFAE.

RESULTS

CFAE were observed at 91 sites, most of which showed discrete MAPs and (1) pansystolic local activity (8%); (2) CFAE after AF acceleration, often with MAP alternans (8%); or (3) nonlocal (far-field) signals (67%). A fourth CFAE pattern lacked discrete MAPs (17%), consistent with spatial disorganization. CFAE with discrete MAPs and pansystolic activation (consistent with rapid localized AF sites) had shorter cycle length (P <.05) and lower voltage (P <.05) and trended to have higher dominant frequency than other CFAE sites. Many CFAEs, particularly at the septa and coronary sinus, represented far-field signals.

CONCLUSION

CFAEs in human AF represent distinct functional types that may be separated using MAPs and activation sequence. In a minority of cases, CFAEs indicate localized rapid AF sites. The majority of CFAEs reflect far-field signals, AF acceleration, or disorganization. These results may help to interpret CFAE during AF substrate mapping.

Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.

Transmural characteristics of atrial fibrillation in canine models of structural and electrical atrial remodeling assessed by simultaneous epicardial and endocardial mapping

BACKGROUND

Epicardial mapping has shown that atrial substrate may play a role in the characteristics of the resulting atrial fibrillation (AF). However, it is not known whether these differences also occur in 3 dimensions.

OBJECTIVE

This study sought to examine the 3-dimensional characteristics of AF by simultaneously analyzing AF on the epicardial and endocardial surfaces.

METHODS

Dogs were divided into 5 groups: congestive heart failure (CHF), rapid atrial pacing (RAP), mitral regurgitation (MR), control, and methylcholine. A noncontact mapping catheter (Ensite 3000 [Endocardial Solutions, Inc., St. Paul, Minnesota]) was placed in the left atrium (LA), and electrode plaques (240 unipoles) were placed over the epicardial surface. Several AF episodes of at least 30 s were recorded, and isopotential videos of activation and isochronal maps were constructed. In addition, each pair of matched electrograms were cross-correlated (XC) and analyzed with a fast Fourier transform (FFT).

RESULTS

The RAP model was the only one with an AF mechanism of multiple wavelets in every dog on both surfaces. In addition, when individual signals were compared, the RAP model had the least amount of similarities between the recording surfaces, whereas the CHF model had the most as it had a higher percentage of signals with XC coefficients >0.8 and a higher percentage of signals with similar dominant frequencies (30 +/- 35% vs. 12 +/- 13% and 66 +/- 30% vs. 26 +/- 10%, P < .05).

CONCLUSION

Although the RAP model had similar AF mechanisms in 3 dimensions, this did not correlate to transmural similarities. Focal mechanisms of AF may have a more uniform wavefront of activation, whereas models with mechanisms of multiple wavelets may have more 3-dimensional properties.

Atrial fibrillation disorganization is reduced by catheter ablation: a standard ECG study

Selection of candidates to catheter ablation (CA) of long-lasting persistent atrial fibrillation (AF) is challenging, since success is not guaranteed. In this study, we put forward an automated method for noninvasively evaluating the reduction of the complexity of the AF organization following CA. Complexity is meant as the amount of disorganization observed on the ECG, supposed to be directly correlated to the number and interactions of atrial wavefronts. By means of PCA, the complexity of the AF organization is evaluated quantitatively from a 12-lead ECG recording. Preliminary results show that CA is able to reduce the complexity of AF organization in the atrial wavefront pattern propagation, despite the persistence of AF in most cases. This can be viewed as a first clinical validation of this parameter. Whether AF complexity and its reduction by CA are predictive of long-term outcome is thus still to be determined.

Increase in organization index predicts atrial fibrillation termination with flecainide post-ablation: spectral analysis of intracardiac electrograms

AIMS

The mechanism of the action of flecainide in the termination of human atrial fibrillation (AF) is not fully understood. We studied the acute effects of flecainide on AF electrograms in the time and frequency domain to identify factors associated with AF termination.

METHODS AND RESULTS

Patients who were still in AF at the end of catheter ablation for AF were given intravenous flecainide. Dominant frequency (DF) and organization index (OI) were obtained by fast Fourier transform of electrograms from the coronary sinus catheter over 10 s in AF, before and after flecainide infusion. Mean AF cycle length (CL) was also calculated. Twenty-six patients were studied (16 paroxysmal AF and 10 persistent AF). Seven converted to sinus rhythm (SR) with flecainide. In all patients, mean CL increased from 211 +/- 44 to 321 +/- 85 ms (P < 0.001). Mean DF decreased from 5.2 +/- 1.03 to 3.6 +/- 1.04 Hz (P < 0.001). Mean OI was 0.33 +/- 0.13 before and 0.32 +/- 0.11 after flecainide (P = 0.90). Comparing patients who converted to SR with those who did not, OI post-flecainide was 0.41 +/- 0.12 vs. 0.29 +/- 0.10 (P = 0.013), and the relative change in OI was 29 +/- 33 vs. -3.9 +/- 27% (P = 0.016), respectively. No significant difference was noted in the change in CL and DF in the two groups.

CONCLUSION

Increase in OI, independent of changes to CL and DF, appears critical to AF termination with flecainide. Increase in OI holds promise as a sensitive predictor of AF termination.

Centrifugal gradients of rate and organization in human atrial fibrillation

INTRODUCTION

Animal studies show that atrial fibrillation (AF) may emanate from sites of high rate and regularity, with fibrillatory conduction to adjacent areas. We used simultaneous mapping to find evidence for potential drivers in human AF defined as sites with higher rate and regularity than surrounding tissue.

MATERIALS AND METHODS

In 24 patients (age 61+/-10 years; 12 persistent), we recorded AF simultaneously from 32 left atrial bipolar basket electrodes in addition to pulmonary veins (PV), coronary sinus, and right atrial electrodes. We measured AF cycle length (CL) by Fourier transform and electrogram regularity at each electrode, referenced to patient-specific atrial anatomy.

RESULTS

We analyzed 10,298 electrode-periods. Evidence for potential AF drivers was found in 11 patients (five persistent). In persistent AF, these sites lay at the coronary sinus and left atrial roof but not PVs, while in paroxysmal AF six of nine sites lay at PVs (P<0.05). During ablation, a subset of patients experienced AF CL prolongation or termination with a focal lesion; in each case this lesion mapped to potential driver sites on blinded analysis. Conversely, sequential mapping failed to reveal these sites, possibly due to fluctuations in dominant frequency at driver locations in the context of migratory AF.

CONCLUSIONS

Simultaneous multisite recordings in human AF reveal evidence for drivers that lie near PVs in paroxysmal but not persistent AF, and were sites where ablation slowed or terminated AF in a subset of patients. The future work should determine if real-time ablation of AF-maintaining regions defined in this fashion eliminates AF.

Frequency analysis of atrial electrograms identifies conduction pathways from the left to the right atrium during atrial fibrillation-studies in two canine models

Studies of atrial fibrillation (AF) have demonstrated that a stable rhythm of very short cycle length in the left atrium (LA) can cause fibrillatory conduction in the rest of the atria. We tested the hypothesis that fast Fourier transform (FFT) analysis of atrial electrograms (AEGs) during this AF will rapidly and reliably identify LA-to-right atrium (RA) conduction pathway(s) generated by the driver.

METHODS AND RESULTS

During induced atrial tachyarrhythmias in the canine sterile pericarditis and rapid ventricular pacing-induced congestive heart failure models, 380-404 AEGs were recorded simultaneously from epicardial electrodes on both atria. FFT analysis of AEGs during AF demonstrated a dominant frequency peak in the LA (driver), and multiple frequency peaks in parts of the LA and the most of the RA. Conduction pathways from the LA driver to the RA varied from study-to-study. They were identified by the presence of multiple frequency peaks with one of the frequency peaks at the same frequency as the driver, and traveled (1) inferior to the inferior vena cava (IVC); (2) between the superior vena cava and the right superior pulmonary vein (RSPV); (3) between the RSPV and the right inferior pulmonary vein (RIPV); (4) between the RIPV and the IVC; and (5) via Bachmann's bundle. Conduction pathways identified by FFT analysis corresponded to the conduction pathways found in classical sequence of activation mapping. Computation time for FFT analysis for each AF episode took less than 5 minutes.

CONCLUSION

FFT analysis allowed rapid and reliable detection of the LA-to-RA conduction pathways in AF generated by a stable and rapid LA driver.

Spatial characteristics of atrial fibrillation electrocardiograms

BACKGROUND

The present study investigates spatial properties of atrial fibrillation (AF) by analyzing vectorcardiogram loops synthesized from 12-lead electrocardiograms (ECGs).

METHODS

After atrial signal extraction, spatial properties are characterized through analysis of successive, fixed-length signal segments and expressed in loop orientation, that is, azimuth and elevation, as well as in loop morphology, that is, planarity and planar geometry. It is hypothesized that more organized AF, expressed by a lower AF frequency, is associated with decreased variability in loop morphology. Atrial fibrillation frequency is determined using spectral analysis.

RESULTS

Twenty-six patients with chronic AF were analyzed using 60-second ECG recordings. Loop orientation was similar when determined from either entire 60- or 1-second segments. For 1-second segments, the correlation between AF frequency and the parameters planarity and planar geometry were 0.608 (P < .001) and 0.543 (P < .005), respectively.

CONCLUSIONS

Quantification of AF organization based on AF frequency and spatial characteristics from the ECG is possible. The results suggested a relatively weak coupling between loop morphology and AF frequency when determined from the surface ECG.