Temporal and Spatial Phase Analyses of the Electrocardiogram Stratify Intra-Atrial and Intra-Ventricular Organization

A novel framework for noninvasive analysis of short-term atrial activity dynamics during persistent atrial fibrillation

ECG-based representation of atrial fibrillation (AF) progression is currently limited. We propose a novel framework for a more sensitive noninvasive characterization of the AF substrate during persistent AF. An atrial activity (AA) recurrence signal is computed from body surface potential map (BSPM) recordings, and a set of characteristic indices is derived from it which captures the short- and long-term recurrent behaviour in the AA patterns. A novel measure of short- and long-term spatial variability of AA propagation is introduced, to provide an interpretation of the above indices, and to test the hypothesis that the variability in the oscillatory content of AA is due mainly to a spatially uncoordinated propagation of the AF waveforms. A simple model of atrial signal dynamics is proposed to confirm this hypothesis, and to investigate a possible influence of the AF substrate on the short-term recurrent behaviour of AA propagation. Results confirm the hypothesis, with the model also revealing the above influence. Once the characteristic indices are normalized to remove this influence, they show to be significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. Therefore, the proposed framework improves noninvasive AF substrate characterization in patients with a very similar substrate. Graphical Abstract Schematic representation of the proposed framework for the noninvasive characterization of short-term atrial signal dynamics during persistent AF. The proposed framework shows that the faster the AA is propagating, the more stable its propagation paths are in the short-term (larger values of Speed in the bottom right plot should be interpreted as lower speed of propagation of the corresponding AA propagation patters).

Spatial relationship of sites for atrial fibrillation drivers and atrial tachycardia in patients with both arrhythmias

INTRODUCTION

Atrial fibrillation (AF) often converts to and from atrial tachycardia (AT), but it is undefined if these rhythms are mechanistically related in such patients. We tested the hypothesis that critical sites for AT may be related to regional AF sources in patients with both rhythms, by mapping their locations and response to ablation on transitions to and from AF.

METHODS

From 219 patients undergoing spatial mapping of AF prior to ablation at 3 centers, we enrolled 26 patients in whom AF converted to AT by ablation (n=19) or spontaneously (n=7; left atrial size 42±6cm, 38% persistent AF). Both atria were mapped in both rhythms by 64-electrode baskets, traditional activation maps and entrainment.

RESULTS

Each patient had a single mapped AT (17 reentrant, 9 focal) and 3.7±1.7 AF sources. The mapped AT spatially overlapped one AF source in 88% (23/26) of patients, in left (15/23) or right (8/23) atria. AF transitioned to AT by 3 mechanisms: (a) ablation anchoring AF rotor to AT (n=13); (b) residual, unablated AF source producing AT (n=6); (c) spontaneous slowing of AF rotor leaving reentrant AT at this site without any ablation (n=7). Electrogram analysis revealed a lower peak-to-peak voltage at overlapping sites (0.36±0.2mV vs 0.49±0.2mV p=0.03).

CONCLUSIONS

Mechanisms responsible for AT and AF may arise in overlapping atrial regions. This mechanistic inter-relationship may reflect structural and/or functional properties in either atrium. Future work should delineate how acceleration of an organized AT may produce AF, and whether such regions can be targeted a priori to prevent AT recurrence post AF ablation.

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.

A new algorithm to diagnose atrial ectopic origin from multi lead ECG systems--insights from 3D virtual human atria and torso

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms.

Rotor stability separates sustained ventricular fibrillation from self-terminating episodes in humans

OBJECTIVES

This study mapped human ventricular fibrillation (VF) to define mechanistic differences between episodes requiring defibrillation versus those that spontaneously terminate.

BACKGROUND

VF is a leading cause of mortality; yet, episodes may also self-terminate. We hypothesized that the initial maintenance of human VF is dependent upon the formation and stability of VF rotors.

METHODS

We enrolled 26 consecutive patients (age 64 ± 10 years, n = 13 with left ventricular dysfunction) during ablation procedures for ventricular arrhythmias, using 64-electrode basket catheters in both ventricles to map VF prior to prompt defibrillation per the institutional review board-approved protocol. A total of 52 inductions were attempted, and 36 VF episodes were observed. Phase analysis was applied to identify biventricular rotors in the first 10 s or until VF terminated, whichever came first (11.4 ± 2.9 s to defibrillator charging).

RESULTS

Rotors were present in 16 of 19 patients with VF and in all patients with sustained VF. Sustained, but not self-limiting VF, was characterized by greater rotor stability: 1) rotors were present in 68 ± 17% of cycles in sustained VF versus 11 ± 18% of cycles in self-limiting VF (p < 0.001); and 2) maximum continuous rotations were greater in sustained (17 ± 11, range 7 to 48) versus self-limiting VF (1.1 ± 1.4, range 0 to 4, p < 0.001). Additionally, biventricular rotor locations in sustained VF were conserved across multiple inductions (7 of 7 patients, p = 0.025).

CONCLUSIONS

In patients with and without structural heart disease, the formation of stable rotors identifies individuals whose VF requires defibrillation from those in whom VF spontaneously self-terminates. Future work should define the mechanisms that stabilize rotors and evaluate whether rotor modulation may reduce subsequent VF risk.

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.

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.

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

OBJECTIVES

The purpose of this study was to separate atrial flutter (AFL) with atypical F waves from fibrillation (AF) with "apparent organization."

BACKGROUND

We hypothesized that F-wave spectra should reveal a dominant and narrow peak in AFL, reflecting its single macro-re-entrant wave front, but broad spectra in AF, reflecting multiple wave fronts.

METHODS

We identified 39 patients with electrocardiograms (ECGs) of "AFL/AF" or "coarse AF" from 134 consecutive patients referred for ablation: 21 had AFL (18 atypical, 3 typical), 18 had AF, and all were successfully ablated. Filtered atrial ECGs were created by cross-correlating F waves to successive ECG time points. Dominant peaks between 3 and 10 Hz were identified from power spectra of X (lead V5), Y (aVF), and Z (V1) axes, and for each, we calculated height (relative to two adjacent spectral points) and area ratio to envelopes of bandwidth 0.625, 1.25, 2.5, 3.75, and 5 Hz (range 0 to 1, where higher ratios reflect narrower peaks).

RESULTS

Dominant peaks had greater relative height for AFL than AF (three-axis mean: 14.2 +/- 6.4 dB vs. 6.6 +/- 2.1 dB; p < 0.001). Peak area ratios were also higher for AFL than AF for all envelopes (p < 0.001). For the 2.5-Hz envelope, the separation (0.61 +/- 0.14 vs. 0.35 +/- 0.05, respectively; p < 0.001) enabled a ratio > or =0.44 to identify all cases of AFL from AF (p < 0.001). A panel of seven cardiologists blinded to clinical data provided lower diagnostic accuracy (82.1%; p < 0.01).

CONCLUSIONS

In ambiguous ECGs with atypical F waves, spectral evidence for a solitary activation cycle separates AFL from AF with "apparent organization." This approach might improve bedside ECG diagnosis and shed light on intra-atrial organization of both rhythms.

Separating non-isthmus- from isthmus-dependent atrial flutter using wavefront variability

OBJECTIVES

The aim of this study was to separate isthmus-dependent atrial flutter (IDAFL) from non-isthmus-dependent atrial flutter (NIDAFL) from the electrocardiogram (ECG) based on functional differences.

BACKGROUND

The ECG analyses of F-wave shape suboptimally separate NIDAFL from IDAFL. The authors hypothesized that anatomic and functional differences may result in greater wavefront variability in NIDAFL than IDAFL, allowing their separation. The authors tested this hypothesis in patients undergoing ablation for atrial flutter using a novel ECG algorithm to detect subtle F-wave variability, validated by intracardiac measurements.

METHODS

In 62 patients (23 NIDAFL, 39 IDAFL) ECG atrial wavefronts were represented as correlations of an F-wave template to the ECG over time. Correlations in orthogonal ECG lead-pairs were plotted at each time point to yield loops reflecting temporal and spatial regularity in each plane. The ECG analyses were compared with intracardiac standard deviations of: 1) atrial electrograms (temporal variability), and 2) bi-atrial activation time differences (spatial variability).

RESULTS

Atrial ECG temporospatial loops were reproducible in IDAFL, but varied in NIDAFL (p < 0.01) suggesting greater variability that correctly classified IDAFL (39 of 39 cases) from NIDAFL (22 of 23 cases; p < 0.001). Intra-atrial mapping confirmed greater temporal variability for NIDAFL versus IDAFL, in lateral (p < 0.01) and septal (p = 0.03) right atrium, and proximal (p = 0.02) and distal (p < 0.01) coronary sinus. Spatial variability was greater in NIDAFL than IDAFL (p = 0.02).

CONCLUSIONS

Greater cycle-to-cycle atrial wavefront variability separates NIDAFL from IDAFL and is detectable from the ECG using temporospatial analyses. These results have implications for guiding ablation and support the concept that IDAFL and NIDAFL lie along a spectrum of intracardiac organization.