Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as 'high rate events'. This may delay or misdirect therapy. Objective: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. Methods: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL using computer models to assess the impact of controlled variations in shape, rate and timing on AF/AT classification in 246,067 EGMs reconstructed from clinical data. Results: DL identified AF with AUC of 0.97 ± 0.04 (unipolar) and 0.92 ± 0.09 (bipolar). Rule-based classifiers misclassified ∼10-12% of cases. DL classification was explained by regularity in EGM shape (13%) or timing (26%), and rate (60%; p 15% timing variation, <0.48 correlation in beat-to-beat EGM shapes and CL < 190 ms (p < 0.001). Conclusions: Deep learning of intracardiac EGMs can identify AF or AT via signatures of rate, regularity in timing or shape, and specific EGM shapes. Future work should examine if these signatures differ between different clinical subpopulations with AF

Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry

Background: The rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or 'unfolding' into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces. Methods: We tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by non-proprietary software to identify the tips of spiral waves, indicated by phase singularities. The number and density of spiral tips were compared in patient-specific 3D shells constructed from the basket, as well as 3D maps from clinical electroanatomic mapping systems and 2D maps. Results: Patients (59.4±12.7 yrs, 60% M) showed 1.7±0.8 phase singularities/patient, in whom ablation terminated AF in 11/20 patients (55%). There was no difference in the location of phase singularities, between 3D curved surfaces and 2D unfolded surfaces, with a median correlation coefficient between phase singularity density maps of 0.985 (0.978-0.990). No significant impact was noted by phase singularities location in more curved regions or relative to the basket location (p>0.1). Conclusions: AF maps and phase singularities mapped by endocardial baskets are qualitatively and quantitatively similar whether calculated by 3D phase maps on patient-specific curved atrial geometries or in 2D. Phase maps on patient-specific geometries may be easier to interpret relative to critical structures for ablation planning

Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping

Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods: In 47 patients with AF (27 persistent, age 63 ± 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) analysis from intracardiac and non-invasive recordings. Results: Raw intracardiac and non-invasive DF differed substantially, by 0.54 Hz [0.13 - 1.37] across bi-atrial regions (R2 = 0.11). Filtering by high spectral organization reduced this difference to 0.10 Hz (cycle length difference of 1 - 11 ms) [0.03 - 0.42] for patient-level comparisons (R2 = 0.62), and 0.19 Hz [0.03 - 0.59] and 0.20 Hz [0.04 - 0.61] for median and highest DF, respectively. Non-invasive and highest DF predicted acute ablation success (p = 0.04). Conclusion: Non-invasive estimation of atrial activation rates is feasible and, when filtered by high spectral organization, provide a moderate estimate of intracardiac recording rates in AF. Non-invasive technology could be an effective tool to identify patients who may respond to AF ablation for personalized therapy

Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can further improve the prediction of patient outcomes

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum