Atrial Conduction Velocity Correlates with Frequency Content of Bipolar Signal

Conduction time around the mitral valve annulus has the potential to rule out postablation perimitral atrial tachycardia

INTRODUCTION

It would be helpful in determining ablation strategy if the occurrence of perimitral atrial tachycardia (PMAT) could be predicted in advance. We investigated whether estimated perimitral conduction time (E-PMCT), namely, twice the time between coronary sinus (CS) pacing and the ensuing wave-front collision at the opposite side of the mitral annulus, correlated with the cycle length of PMAT and could predict future PMAT.

METHODS AND RESULTS

We retrospectively (retrospective cohort) and prospectively (validation cohort) investigated atrial fibrillation patients who had received pulmonary vein isolation (PVI) and in whom left atrial maps had been created during CS pacing. We calculated their E-PMCT. PMAT was observed either by provocation or during follow-up in 25, AT other than PMAT was observed in 24 (non-PMAT AT group), and 53 patients never displayed any AT (no-AT group) in the retrospective cohort. In the PMAT group of the retrospective cohort, a strong positive correlation was observed between the PMAT CL and E-PMCT (r = .85, p < 0.001). PMAT was never induced nor observed in patients with E-PMCT less than 176 ms, and the best cut-off value for PMAT was 180 ms by receiver-operating characteristic curve analysis. In the validation cohort of 76 patients, the cut-off value of the E-PMAT less than 180 ms predicted noninducibility of PMAT, with a sensitivity of 78.6%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 25.0%.

CONCLUSION

Short E-PMCT may predict noninducibility of PMAT and guide a less invasive ablation strategy.

High predictive value of paced QRS frequency in verification of left bundle branch pacing

BACKGROUND

Left bundle branch area pacing (LBBAP) is a newer technique to deliver more synchronous left ventricular activation. Several criteria have been proposed, but not fully validated, to confirm LBBAP during implantation of the pacing lead. Spectral analysis has been used to characterize the frequency components of the clinical QRS utilizing the Fourier transform algorithm. We hypothesized that higher frequency content of the paced QRS complex may show predictive value of successful LBBAP.

METHODS

We evaluated 84 patients with ejection fraction > 50%, who underwent LBB lead placement (n = 42) using ≥ 1 current criteria and right ventricular midseptal (RVsp) lead placement (n = 42) from 2000 to 2022. Time frequency analysis (Matlab) was used to determine the frequency content of the paced QRS complex. The centroid frequency (CF), which is the weighted average QRS frequency, was calculated.

RESULTS

Patients in RVsp group had a longer paced QRS duration (155.6 ± 28.0 vs 127.1 ± 17.2, p < 0.002) compared to the LBBAP group. Of all standard ECG leads, the paced QRS in V2 gave the greatest difference of the CF of the LBBAP group at 8.8 ± 1.6 Hz versus 5.7 ± 0.7 Hz of the RVsp group. This difference was significant by both univariate (p < 0.003) and multivariate (p < 0.010) analysis. Predictive value of the CF for successful LBB pacing in lead V2 was highest with an AUC of 0.98. The sensitivity and specificity were 88.1% and 97.6%, respectively.

CONCLUSION

Spectral analysis predicts successful LBBAP with higher frequency content when compared to RVsp pacing. Given the limitations to the current criteria to confirm LBBAP, intraprocedural use of frequency content analysis of the paced QRS complex in patients may prove useful at verifying LBB capture if verified by prospective clinical trials.

Cardiac Conduction Velocity Estimation During Wavefront Collision

Catheter ablation therapy is an effective approach to treat different arrhythmias. Cardiac conduction velocity (Cv), extracted from intracardiac electrograms, shows the speed and direction of the wavefront propagation at different sites and is an insightful feature to guide ablation therapy. To create a propagation map, a small mapping catheter with a high density of electrodes is usually used to sequentially collect electrograms from different sites in a desired chamber of the heart. The CV and isochrone surface estimations are very challenging during complex arrhythmias such as atrial fibrillation, where multiple wavefronts simultaneously excite different cardiac sites. Specifically, the performances of CV estimators significantly degrade at catheter sites where wave- fronts collide. This is mainly because during collision, different wavefronts pass the areas under different electrodes of the catheter. Consequently, the activation times of the electrodes are the results of different wavefronts, and there are sharp changes in isochrone line patterns in the vicinity of the collision's border. In this paper, we propose a method that is able to identify the collision sites and improve the estimation of CV and isochrone maps. The proposed method finds the electrodes of the catheter that are excited by a similar wavefront and then estimates the corresponding isochrone lines for that wavefront. Our simulation results confirmed the efficiency of the proposed method during collision.

Muscle Thickness and Curvature Influence Atrial Conduction Velocities

Electroanatomical mapping is currently used to provide clinicians with information about the electrophysiological state of the heart and to guide interventions like ablation. These maps can be used to identify ectopic triggers of an arrhythmia such as atrial fibrillation (AF) or changes in the conduction velocity (CV) that have been associated with poor cell to cell coupling or fibrosis. Unfortunately, many factors are known to affect CV, including membrane excitability, pacing rate, wavefront curvature, and bath loading, making interpretation challenging. In this work, we show how endocardial conduction velocities are also affected by the geometrical factors of muscle thickness and wall curvature. Using an idealized three-dimensional strand, we show that transverse conductivities and boundary conditions can slow down or speed up signal propagation, depending on the curvature of the muscle tissue. In fact, a planar wavefront that is parallel to a straight line normal to the mid-surface does not remain normal to the mid-surface in a curved domain. We further demonstrate that the conclusions drawn from the idealized test case can be used to explain spatial changes in conduction velocities in a patient-specific reconstruction of the left atrial posterior wall. The simulations suggest that the widespread assumption of treating atrial muscle as a two-dimensional manifold for electrophysiological simulations will not accurately represent the endocardial conduction velocities in regions of the heart thicker than 0.5 mm with significant wall curvature.

Inflection point analysis: A machine learning approach for extraction of IEGM active intervals during atrial fibrillation

OBJECTIVE

In this paper, we propose a novel algorithm to extract the active intervals of intracardiac electrograms during atrial fibrillation.

METHODS

First, we show that the characteristics of the signal waveform at its inflection points are prominent features that are implicitly used by human annotators for distinguishing between active and inactive intervals of IEGMs. Then, we show that the natural logarithm of features corresponding to active and inactive intervals exhibits a mixture of two Gaussian distributions in three dimensional feature space. An Expectation Maximization algorithm for Gaussian mixtures is then applied for automatic clustering of the features into two categories.

RESULTS

The absolute error in onset and offset estimation of active intervals is 6.1ms and 10.7ms, respectively, guaranteeing a high resolution. The true positive rate for the proposed method is also 98.1%, proving the high reliability.

CONCLUSION

The proposed method can extract the active intervals of IEGMs during AF with a high accuracy and resolution close to manually annotated results.

SIGNIFICANCE

In contrast with some of the conventional methods, no windowing technique is required in our approach resulting in significantly higher resolution in estimating the onset and offset of active intervals. Furthermore, since the signal characteristics at inflection points are analyzed instead of signal samples, the computational time is significantly low, ensuring the real-time application of our algorithm. The proposed method is also robust to noise and baseline variations thanks to the Laplacian of Gaussian filter employed for extraction of inflection points.