Separating atrial flutter from atrial fibrillation with apparent electrocardiographic organization using dominant and narrow F-wave spectra

Expert-enhanced machine learning for cardiac arrhythmia classification

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) from regular atrial tachycardias including atrial flutter (AFlu) based on a surface electrocardiogram (ECG). Recently, many approaches for an automatic classification of cardiac arrhythmia were proposed and to our knowledge none of them can distinguish between these two. We discuss reasons why deep learning may not yield satisfactory results for this task. We generate new and clinically interpretable features using mathematical optimization for subsequent use within a machine learning (ML) model. These features are generated from the same input data by solving an additional regression problem with complicated combinatorial substructures. The resultant can be seen as a novel machine learning model that incorporates expert knowledge on the pathophysiology of atrial flutter. Our approach achieves an unprecedented accuracy of 82.84% and an area under the receiver operating characteristic (ROC) curve of 0.9, which classifies as "excellent" according to the classification indicator of diagnostic tests. One additional advantage of our approach is the inherent interpretability of the classification results. Our features give insight into a possibly occurring multilevel atrioventricular blocking mechanism, which may improve treatment decisions beyond the classification itself. Our research ideally complements existing textbook cardiac arrhythmia classification methods, which cannot provide a classification for the important case of AFib↔AFlu. The main contribution is the successful use of a novel mathematical model for multilevel atrioventricular block and optimization-driven inverse simulation to enhance machine learning for classification of the arguably most difficult cases in cardiac arrhythmia. A tailored Branch-and-Bound algorithm was implemented for the domain knowledge part, while standard algorithms such as Adam could be used for training.

Electrocardiographic spatial loops indicate organization of atrial fibrillation minutes before ablation-related transitions to atrial tachycardia

BACKGROUND

During ablation for atrial fibrillation (AF), it is challenging to anticipate transitions to organized tachycardia (AT). Defining indices of this transition may help to understand fibrillatory conduction and help track therapy.

OBJECTIVE

To determine the timescale over which atrial fibrillation (AF) organizes en route to atrial tachycardia (AT) using the ECG referenced to intracardiac electrograms.

METHODS

In 17 AF patients at ablation (58.7±9.6years; 53% persistent AF) we analyzed spatial loops of atrial activity on the ECG and intracardiac electrograms over successive timepoints. Loops were tracked at precisely 15, 10, 5, 3 and 1min prior to defined transitions of AF to AT.

RESULTS

Organizational indices reliably quantified changes from AF to AT. Spatiotemporal AF organization on the ECG was identifiable at least 15min before AT was established (p=0.02).

CONCLUSIONS

AF shows anticipatory global organization on the ECG minutes before AT is clinically evident. These results offer a foundation to establish when AF therapy is on an effective path, and for a quantitative classification separating AT from AF.

Dual-chamber implantable cardioverter-defibrillator. Is it useful in patient with permanent atrial fibrillation?

In patients with permanent atrial fibrillation (AF) and implantable cardioverter–defibrillator (ICD) implant indication, a single‐chamber device is the choice because AF does not provide interesting information for the treatment. It is very unusual to find patients with permanent AF that coexist with atrial tachycardia with various degree of Atrioventricular block.

Atrial Tachycardias After Atrial Fibrillation Ablation Manifest Different Waveform Characteristics: Implications for Characterizing Tachycardias

INTRODUCTION

Atrial fibrillation (AF) ablation patients often manifest atrial tachycardias (AT) with atypical ECG morphologies that preclude accurate localization and mechanism. Diagnostic maneuvers used to define ATs during electrophysiology studies can be limited by tachycardia termination or transformation. Additional methods of characterizing post-AF ablation ATs are required.

METHODS AND RESULTS

We evaluated the utility of noninvasive ECG signal analytics in postablation AF patients for the following features: (1) Localization of ATs (i.e., right vs. left atrium), and (2) Identification of common left AT mechanisms (i.e., focal vs. macroreentrant). Atrial waveforms from the surface ECG were used to analyze (1) spectral organization, including dominant amplitude (DA) and mean spectral profile (MP), and (2) temporospatial variability, using temporospatial correlation coefficients. We studied 94 ATs in 71 patients who had undergone prior pulmonary vein isolation for AF and returned for a second ablation: (1) right atrial cavotricuspid-isthmus dependent (CTI) ATs (n = 21); (2) left atrial macroreentrant ATs (n = 41) and focal ATs (n = 32). Right CTI ATs manifested higher DAs and lower MPs than left ATs, indicative of greater stability and less complexity in the frequency spectrum. Left macroreentrant ATs possessed higher temporospatial organization than left focal ATs.

CONCLUSIONS

Noninvasively recorded atrial waveform signal analyses show that right ATs possess more stable activation properties than left ATs, and left macroreentrant ATs manifest higher temporospatial organization than left focal ATs. Further prospective analyses evaluating the role these novel ECG-derived tools can play to help localize and identify mechanisms of common ATs in AF ablation patients are warranted.

Study of atrial activities for abnormality detection by phase rectified signal averaging technique

Non-invasive detection of Atrial Fibrillation (AF) and Atrial Flutter (AFL) from ECG at the time of their onset can prevent forthcoming dangers for patients. In most of the previous detection algorithms, one of the steps includes filtering of the signal to remove noise and artefacts present in the signal. In this paper, a method of AF and AFL detection is proposed from ECG without the conventional filtering stage. Here Phase Rectified Signal Average (PRSA) technique is used with a novel optimized windowing method to achieve an averaged signal without quasi-periodicities. Both time domain and statistical features are extracted from a novel SQ concatenated section of the signal for non-linear Support Vector Machine (SVM) based classification. The performance of the proposed algorithm is tested with the MIT-BIH Arrhythmia database and good performance parameters are obtained, as indicated in the result section.

Discriminating atrial flutter from atrial fibrillation using a multilevel model of atrioventricular conduction

BACKGROUND

The discrimination between atrial flutter (AFlu) and atrial fibrillation (AFib) can be made difficult by an irregular ventricular response owing to complex conduction phenomena within the atrioventricular (AV) node, known as multilevel AV block. We tested the hypothesis that a mathematical algorithm might be suitable to discriminate both arrhythmias.

OBJECTIVES

To discriminate AFlu with irregular ventricular response from AFib based on the sequence of R-R intervals.

METHODS

Intracardiac recordings of 100 patients (50 patients with AFib and 50 patients with AFlu) were analyzed. On the basis of a numerical simulation of variable flutter frequencies followed by 2 levels of AV block in series, a given sequence of R-R intervals was analyzed.

RESULTS

Although the ventricular response displays absolute irregularity in AFib, the sequences of R-R intervals follow certain rules in AFlu. We find that using a mathematical simulation of multilevel AV block, based on the R-R sequence of 16 ventricular beats, a stability of atrial activation could be predicted with a sensitivity of 84% and a specificity of 74%. When limiting the ventricular rate to 125 beats/min, discrimination could be performed with a sensitivity of even 89% and a specificity of 80%. In cases of AFlu, the atrial cycle length could be predicted with high accuracy.

CONCLUSION

On the basis of the electrophysiological mechanism of multilevel AV block, we developed a computer algorithm to discriminate between AFlu and Afib. This algorithm is able to predict the stability and cycle length of atrial activation for short R-R sequences with high accuracy.

Impact of a 4q25 genetic variant in atrial flutter and on the risk of atrial fibrillation after cavotricuspid isthmus ablation

BACKGROUND

The prediction of atrial fibrillation (AF) following catheter ablation of atrial flutter (Afl) would be helpful to facilitate targeted arrhythmia monitoring and anti-coagulation strategies. A single nucleotide polymorphism, rs2200733, is strongly associated with AF. We sought to characterize the association between rs2200733 and prevalent Afl and to determine if the variant could predict AF after cavotricuspid isthmus ablation.

METHODS AND RESULTS

We performed a genetic association study of 295 patients with Afl and/or AF and 469 controls using multivariable logistic regression. The variant was then assessed as a predictor of incident AF after cavotricuspid isthmus ablation in 87 consecutive typical Afl patients with Cox proportional hazards models. The rs2200733 rare allele was associated with an adjusted 2.06-fold increased odds of isolated Afl (95% CI: 1.13-3.76, P = 0.019) and an adjusted 2.79-fold increased odds of a combined phenotype of AF and Afl (95% CI: 1.81-4.28, P < 0.001). Following catheter ablation for Afl, carrier status of rs2200733 failed to predict an increased risk of AF either among all subjects (adjusted HR: 0.94; 95% CI: 0.58-1.53, P = 0.806) or among those with isolated Afl (adjusted HR: 1.29; 95% CI: 0.51-3.26, P = 0.585).

CONCLUSIONS

Our study demonstrates that Afl, whether occurring in isolation or along with AF, is associated with the rs2200733 AF risk allele. Genetic carrier status of rs2200733 failed to predict an increased risk of incident or recurrent AF following catheter ablation for Afl. These findings suggest that the causal mechanism associated with rs2200733 is germane to both AF and Afl.

Non-invasive identification of stable rotors and focal sources for human atrial fibrillation: mechanistic classification of atrial fibrillation from the electrocardiogram

AIMS

To develop electrocardiogram (ECG) tools to quantify the number of sources for atrial fibrillation (AF), i.e. spatially stable rotors and focal impulses, and whether they lie in right or left atrium. Intracardiac mapping has recently shown that paroxysmal and persistent AF is sustained by rotors or focal sources that are stable in location and thus targets for limited ablation [focal impulse and rotor modulation (FIRM)] to eliminate AF. Importantly, the numbers and locations of concurrent sources determine both the complexity of AF and the approach for ablation.

METHODS AND RESULTS

In 36 AF patients (n = 29 persistent, 63 ± 9 years) in the CONventional ablation with or without Focal Impulse and Rotor Modulation (CONFIRM) trial, we developed phase lock (PL) to quantify spatial repeatability of ECG 'F-waves' between leads over time. Phase lock spectrally quantifies the angle θ between F-wave voltages in planes formed by ECG leads I, aVF, and V1 at successive points in time. We compared PL with ECG spectral dominant frequency (DF) and organizational index (OI) to characterize stable rotors and focal sources validated by intracardiac FIRM mapping. Focal impulse and rotor modulation ablation alone at ≤3 sources acutely terminated and rendered AF non-inducible or substantially slowed AF in 31 of 36 patients. Receiver operating characteristics of PL for this endpoint had area under the curve (AUC) = 0.72, and the optimum cut-point (PL = 0.09) had 74% sensitivity, 92% positive predictive value (PPV). Receiver operating characteristics areas for OI and DF were 0.50 and 0.58, respectively. Left (n = 28) or right (n = 3) atrial sources were localized by PL with AUC = 0.85, sensitivity 100%, PPV 30%, and negative predictive value 100%. Spectral DF provided AUC = 0.79. Notably, PL did not comigrate with diagnosis of paroxysmal or persistent AF (P = NS), unlike ECG DF.

CONCLUSION

The novel metric of ECG PL identifies patients with fewer (≤3) or greater numbers of stable rotors/focal sources for AF, validated by intracardiac FIRM mapping, and localized them to right or left atria. These data open the possibility of using 12-lead ECG analyses to classify AF mechanistically and plan procedures for right- or left-sided FIRM ablation.

AUTOMATED DETECTION OF ATRIAL FLUTTER AND FIBRILLATION USING ECG SIGNALS IN WAVELET FRAMEWORK

In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers.

Distribution of Mean Cycle Length in Cavo-Tricuspid Isthmus Dependent Atrial Flutter

Although cycle length (CL) constitutes a fundamental descriptor of any arrhythmia, there is not larger study describing mean CL in electrophysiologically confirmed cavo-tricuspid isthmus (CTI)-dependent atrial flutter (AFL). We analyzed retrospectively digital recordings of 121 patients (98 men; age 64±11 years) referred for radiofrequency ablation of persistent CTI-dependent AFL. Median of mean AFL CL was 240 ms (interquartile range (IQR) of 222-258 ms, overall range of 178-399 ms). The distribution of CL was not normal (Shapiro Wilk test, p<0.001). Both counterclockwise and clockwise (14.9 % of all cases) AFLs were comparable in their CL; 240 (IQR 222-258) ms vs. 234 (217-253) ms, respectively. AFL CL<200 ms and AFL CL<190 ms was noticed in 5 (4.1 %) and 3 cases (2.5 %), respectively. In multivariate regression analysis, age (increase by 6±3 ms per decade of age, p=0.036), treatment with specific antiarrhythmic drugs (increase by 11±6 ms, p=0.052) and the history of cardiac surgery (increase by 26±9 ms, p=0.004) were independently associated with AFL CL. In conclusions, the distribution of AFL CL is not normal. The prevalence of AFL with short CL is low. Short CL<200 ms does not rule out the CTI-dependent AFL, especially in young and otherwise healthy patients.

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.

Accurate ECG diagnosis of atrial tachyarrhythmias using quantitative analysis: a prospective diagnostic and cost-effectiveness study

Quantitative ECG Analysis.

INTRODUCTION

Optimal atrial tachyarrhythmia management is facilitated by accurate electrocardiogram interpretation, yet typical atrial flutter (AFl) may present without sawtooth F-waves or RR regularity, and atrial fibrillation (AF) may be difficult to separate from atypical AFl or rapid focal atrial tachycardia (AT). We analyzed whether improved diagnostic accuracy using a validated analysis tool significantly impacts costs and patient care.

METHODS AND RESULTS

We performed a prospective, blinded, multicenter study using a novel quantitative computerized algorithm to identify atrial tachyarrhythmia mechanism from the surface ECG in patients referred for electrophysiology study (EPS). In 122 consecutive patients (age 60 ± 12 years) referred for EPS, 91 sustained atrial tachyarrhythmias were studied. ECGs were also interpreted by 9 physicians from 3 specialties for comparison and to allow healthcare system modeling. Diagnostic accuracy was compared to the diagnosis at EPS. A Markov model was used to estimate the impact of improved arrhythmia diagnosis. We found 13% of typical AFl ECGs had neither sawtooth flutter waves nor RR regularity, and were misdiagnosed by the majority of clinicians (0/6 correctly diagnosed by consensus visual interpretation) but correctly by quantitative analysis in 83% (5/6, P = 0.03). AF diagnosis was also improved through use of the algorithm (92%) versus visual interpretation (primary care: 76%, P < 0.01). Economically, we found that these improvements in diagnostic accuracy resulted in an average cost-savings of $1,303 and 0.007 quality-adjusted-life-years per patient.

CONCLUSIONS

Typical AFl and AF are frequently misdiagnosed using visual criteria. Quantitative analysis improves diagnostic accuracy and results in improved healthcare costs and patient outcomes.

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.

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.

Electrocardiographic measurements of regional atrial fibrillation cycle length

BACKGROUND

Differences in atrial fibrillation (AF) cycle length (CL) between the left (LA) and right (RA) atrium and coronary sinus (CS) may help separate paroxysmal from persistent AF and identify patients most likely to respond to pulmonary vein isolation, but has not been measured noninvasively.

METHODS AND RESULTS

We developed methods to estimate regional intraatrial AF CL from the surface electrocardiogram (ECG) in 20 patients with persistent AF and 10 patients with paroxysmal AF prior to ablation. Intraatrial AF CL was measured near the LA appendage, mid-CS, and lateral RA. In simultaneous filtered ECG, AF CL was estimated using autocorrelation. The mean of ECG-derived AF CL in leads V5, I, and aVL was used to estimate LA CL; leads aVF, II, and III for CS CL; and V1, V2, and aVR for RA CL. ECG CL estimates for the LA, CS, and RA had R(2) > 0.91 versus measured CL (all P < 0.001). Though magnitudes of left-versus-right AF CL gradients were small in this series, the ECG predicted the direction of gradients in 62% of measurements (P < 0.05). When the gradient was >10 ms, the direction was accurately predicted in eight of 11 patients. The accuracy of AF CL estimates was not adversely affected by AF type or LA dilatation (< or =40 or >40 mm). The ECG-estimated AF-CL showed high 5-minute temporal stability (P < 0.001 each chamber).

CONCLUSIONS

Left and right atrial AF CL, and their gradients, can be accurately determined from the ECG using autocorrelation analysis. This approach may be a helpful guide prior to ablation procedures.

Using electrocardiographic activation time and diastolic intervals to separate focal from macro-re-entrant atrial tachycardias

OBJECTIVES

This study was designed to separate focal from atypical macro-re-entrant atrial tachycardia (AT) on the electrocardiogram (ECG).

BACKGROUND

Focal AT often cannot be distinguished from macro-re-entrant AT until the time of electrophysiology study (EPS). We hypothesized that quantitative ECG metrics should separate focal AT, using its short activation relative to tachycardia cycle length (CL), from macro-re-entrant AT, whose activation should span the CL. We developed tools to accurately quantify CL and P- or F-wave duration even when overlying T waves, then prospectively applied them to patients during focal or macro-re-entrant AT ablation and compared them to the gold standard EPS diagnosis.

METHODS

We studied 41 patients (27 men, 14 women) age 57 +/- 17 years. In the training group (n = 20), tachycardia P or F waves overlying T waves were identified from transitions in slope (dV/dt) relative to "expected" T waves generated from scaling of the sinus-rate T-wave. Electrocardiographic P-wave duration agreed with the duration of intra-atrial activation. Autocorrelation was used to estimate ECG atrial CL (p < 0.001).

RESULTS

Compared to macro-re-entry (n = 13), focal AT (n = 7) had shorter P waves (115 +/- 31 ms vs. 227 +/- 67 ms; p < 0.001) that were smaller ratios of CL (28 +/- 7% vs. 85 +/- 21%; p < 0.001). Receiver-operating characteristic curve areas for AT were 0.92 for P(F)-wave duration and 0.99 for P(F)/CL ratio. On blinded prospective analysis (n = 21), P(F)-wave duration <160 ms identified focal (n = 7) from macro-re-entrant AT (n = 14) with 90% sensitivity and 90% specificity, and a P(F)/CL ratio <45% gave 86% sensitivity and 98% specificity.

CONCLUSIONS

Quantitative ECG indexes of shorter atrial activation and longer diastolic interval separate focal from macro-re-entrant AT without diagnostic maneuvers.

Evaluating fluctuations in human atrial fibrillatory cycle length using monophasic action potentials

OBJECTIVE

To study fluctuations in intracardiac atrial fibrillation (AF) cycle length (CL).

BACKGROUND

Sites of short AF CL may be good ablation targets, and cycle lengthening predicts ablation success. However, the optimum method for measuring AF CL, and its stability, are unclear. We hypothesized that autocorrelation better estimates AF CL than spectral dominant frequency (DF), which is susceptible to double counting, using monophasic action potentials (MAPs) to separate atrial activation from artifact.

METHODS

In 28 patients with paroxysmal or persistent AF, we analyzed 49 AF epochs using MAPs at the high (HRA) and low (LRA) right atrium. We estimated AF CL over 2 seconds, 10 seconds, and 2 minutes using spectral DF and autocorrelation in MAPs and filtered bipoles.

RESULTS

In the HRA, manually measured CL was 167 +/- 25 ms. Spectral DF poorly estimated AF CL in bipolar signals (R = 0.31; P = NS), due to double counting, but accurately estimated MAP CL (R = 0.73, P < 0.001). Autocorrelation estimated MAP (R = 0.92; P < 0.001) and bipolar (R = 0.83; P < 0.001) CL, with lower errors than spectral DF (P < 0.0001). Over time, changes in DF consistently preceded reciprocal changes in organization (P < 0.001). Finally, excluding inaccurate spectra, DF and AF organization differed between HRA and LRA over 2 seconds, but correlated over 10 seconds and 2 minutes (P < 0.05).

CONCLUSIONS

AF CL is better estimated by autocorrelation than spectral DF, particularly for bipoles, and stable when measured for >10 seconds. Notably, changes in AF CL preceded reciprocal changes in organization, yet changes in organization did not precede changes in AF CL. These results may help to interpret AF CL fluctuations.

Localizing circuits of atrial macroreentry using electrocardiographic planes of coherent atrial activation

BACKGROUND

The complexity of ablation for atrial macroreentry atrial flutter (AFL) varies significantly depending on the circuit location. Presently, surface electrocardiogram (ECG) analysis poorly separates left from right atypical AFL and from some cases of typical AFL, which delays diagnosis until invasive study.

OBJECTIVE

The purpose of this study was to differentiate and localize the intra-atrial circuits of left atypical AFL, right atypical, and typical AFL using quantitative ECG analysis.

METHODS

We studied 66 patients (54 men, age 59 +/- 14 years) with typical (n = 35), reverse typical (n = 4), and atypical (n = 27) AFL. For each, we generated filtered atrial waveforms from ECG leads V5 (X-axis), aVF (Y-axis), and V1 (Z-axis) by correlating a 120-ms F-wave sample to successive ECG regions. Atrial spatial loops were plotted for three orthogonal planes (frontal, XY = V5/aVF; sagittal, YZ = aVF/V1; axial, XZ = V5/V1) and then cross-correlated to measure spatial regularity (i.e., coherence; range -1 to 1).

RESULTS

Mean coherence was greatest in the XY plane (P <10(-3) vs. XZ or YZ). Atypical AFL showed lower coherence than typical AFL in the XY (P <10(-3)), YZ (P <10(-6)), and XZ (P <10(-5)) planes. Atypical left AFL could be separated from atypical right AFL by lower XY coherence (P = .02); for this plane, coherence <0.69 detected atypical left AFL with 84% specificity and 75% sensitivity. F-wave amplitude alone did not separate typical, atypical right, or atypical left AFL (P = NS).

CONCLUSIONS

Atypical AFL shows lower spatial coherence than typical AFL, particularly in the sagittal and axial planes. Coherence in the Cartesian frontal plane separated left and right atypical AFL. Such analyses may be used to plan ablation strategy from the bedside.

Diagnostic accuracy of irregularly irregular RR intervals in separating atrial fibrillation from atrial flutter

Periodic electrocardiographic RR intervals are often used to separate atrial flutter (AFl) from atrial fibrillation (AF), but have not yet been validated. We hypothesized that irregularly irregular electrocardiographic RR intervals poorly identify AF from AFl, and that atrial wavefront regularity affects variability in atrioventricular conduction. We studied the electrocardiographic RR intervals in 66 patients referred for ablation of AFl (21 atypical and 21 typical) and AF (n=24) in relation to intracardiac activation. Receiver-operating characteristics showed that periodicity in 60% of RR intervals provided only 66% sensitivity and 86% specificity for typical AFl and failed to separate atypical AFl from AF. Fewer RR intervals were integer atrial/ventricular ratios in atypical than in typical AFl (50% vs 83%, p=0.005), reflecting greater SDs in atrial cycles (6.98+/-7.02 vs 3.10+/-1.62 ms, p=0.023), more variable AH intervals (56+/-28 vs 18+/-21 ms, p=0.002), longer AH intervals (185+/-65 vs 118+/-31 ms, p=0.01), and greater AH variability for any atrial cycle length (p=0.026). In conclusion, irregularly irregular RR intervals are not specific for AF, are common in AFl, and should not be used to conclusively separate AF from AFl in the absence of sawtooth flutter waves. Future studies should explore the relation among AFl circuit location, cycle variability, and atrioventricular nodal conduction.