Electromechanical Cycle Length Mapping for atrial arrhythmia detection and cardioversion success assessment.

BACKGROUND: Direct current cardioversion (DCCV) is an established treatment to acutely convert atrial fibrillation (AF) to normal sinus rhythm. Yet, more than 70% of patients revert to AF shortly thereafter. Electromechanical Cycle Length Mapping (ECLM) is a high framerate, spectral analysis technique shown to non-invasively characterize electromechanical activation in paced canines and re-entrant flutter patients. This study assesses ECLM feasibility to map and quantify atrial arrhythmic electromechanical activation rates and inform on 1-day and 1-month DCCV response. METHODS: Forty-five subjects (30 AF; 15 healthy sinus rhythm (SR) controls) underwent transthoracic ECLM in four standard apical 2D echocardiographic views. AF patients were imaged within 1 h pre- and post-DCCV. 3D-rendered atrial ECLM cycle length (CL) maps and spatial CL histograms were generated. CL dispersion and percentage of arrhythmic CLs≤333ms across the entire atrial myocardium were computed transmurally. ECLM results were subsequently used as indicators of DCCV success. RESULTS: ECLM successfully confirmed the electrical atrial activation rates in 100% of healthy subjects (R2=0.96). In AF, ECLM maps localized the irregular activation rates pre-DCCV and confirmed successful post-DCCV with immediate reduction or elimination. ECLM metrics successfully distinguished DCCV 1-day and 1-month responders from non-responders, while pre-DCCV ECLM values independently predicted AF recurrence within 1-month post-DCCV. CONCLUSIONS: ECLM can characterize electromechanical activation rates in AF, quantify their extent, and identify and predict short- and long-term AF recurrence. ELCM constitutes thus a noninvasive arrhythmia imaging modality that can aid clinicians in simultaneous AF severity quantification, prediction of AF DCCV response, and personalized treatment planning.

A New Electromechanical Wave Imaging Dispersion Metric for the Characterization of Ventricular Activation in Different Cardiac Resynchronization Therapy Pacing Schemes.

Conventional biventricular (BiV) pacing cardiac resynchronization therapy (CRT) is an established treatment for heart failure patients. Recently, multiple novel CRT delivering technologies such as His-Bundle pacing have been investigated as alternative pacing strategies for optimal treatment benefit. Electromechanical Wave Imaging (EWI), a high frame-rate echocardiography-based modality, is capable of visualizing the change from dyssynchronous activation to resynchronized BiV-paced ventricles in 3D. This proof-of-concept study introduces a new EWI-based dispersion metric to further characterize ventricular activation. Patients with His-Bundle device implantation (n = 4), left-bundle branch block (n = 10), right-ventricular (RV) pacing (n = 10), or BiV pacing (n = 15) were imaged, as well as four volunteers in normal sinus rhythm (NSR). EWI successfully mapped the ventricular activation resulting from His-Bundle pacing. Additionally, very similar activation patterns were obtained in the NSR subjects, confirming recovery of physiological activation with His pacing. The dispersion metric was the most sensitive EWI-based metric that identified His pacing as the most efficient treatment (lowest activation time spread), followed by BiV and RV pacing. More specifically, the dispersion metric significantly (p < 0.005) distinguished His pacing from the other two pacing schemes as well as LBBB. The initial findings presented herein indicate that EWI and its new dispersion metric may provide a useful resynchronization evaluation clinical tool in CRT patients under both novel His-Bundle pacing and more conventional BiV pacing strategies.

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation.

Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When considering large populations, this becomes a time-consuming process, that can be limited by inter-observer variability and operator bias. In this study, we developed and optimized an automated ZC selection algorithm, towards a faster more robust isochrone generation approach. The algorithm either relies on heuristic-based baselines or machine learning classifiers. Manually generated isochrones, previously validated against 3D intracardiac mapping, were considered as ground truth during training and performance evaluation steps. The machine learning models applied herein for the first time were: i) logistic regression; ii) support vector machine (SVM); and iii) Random Forest. The SVM and Random Forest classifiers successfully identified accessory pathways in Wolff-Parkinson-White patients, characterized sinus rhythm in humans, and localized the pacing electrode location in left ventricular paced canines on the resulting isochrones. Nevertheless, the best performing classifier was proven to be Random Forest with a precision rising from 89.5% to 97%, obtained with the voting approach that sets a probability threshold upon ZC candidate selection. Furthermore, the predictivity was not dependent on the type of testing dataset it was applied to, contrary to SVM that exhibited a 5% drop in precision on the canine testing dataset. Finally, these findings indicate that a machine learning approach can reduce user variability and considerably decrease the durations required for isochrone generation, while preserving accurate activation patterns.