AimsUse of a non-invasive electrocardiographic mapping system may aid in rapid diagnosis of atrial or ventricular arrhythmias or the detection of ventricular dyssynchrony. The aim of the present study was to validate the mapping accuracy of a novel non-invasive epi- and endocardial electrophysiology system (NEEES).Methods and resultsPatients underwent pre-procedural computed tomography or magnetic resonance imaging of the heart and torso. Radiographic data were merged with the data obtained from the NEEES during pacing from implanted pacemaker leads or pacing from endocardial sites using an electroanatomical mapping system (CARTO 3, Biosense Webster). The earliest activation as denoted on the NEEES three-dimensional heart model was compared with the true anatomic location of the tip of the pacemaker lead or the annotated pacing site on the CARTO 3 map. Twenty-nine patients [mean age: 62 ± 11 years, 6/29 (11%) female, 21/29 (72%) with ischaemic cardiomyopathy] were enrolled into the pacemaker verification group. The mean distance from the non-invasively predicted pacing site to the anatomic reference site was 10.8 ± 5.4 mm for the right atrium, 7.7 ± 5.8 mm for the right ventricle, and 7.9 ± 5.7 mm for the left ventricle activated via the coronary sinus lead. Five patients [mean age 65 ± 4 years, 2 (33%) females] underwent CARTO 3 verification study. The mean distance between non-invasively reconstructed pacing site and the reference pacing site was 7.4 ± 2.7 mm for the right atrium, 6.9 ± 2.3 mm for the left atrium, 6.5 ± 2.1 mm for the right ventricle, and 6.4 ± 2.2 for the left ventricle, respectively.ConclusionThe novel NEEES was able to correctly identify the site of pacing from various endo- and epicardial sites with high accuracy.

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Although model-based solution strategies for the ECGI were reported to deliver promising clinical results, they strongly rely on some a priori assumptions, which do not hold true for many pathological cases. The fastest route algorithm (FRA) is a well-established method for noninvasive imaging of ectopic activities. It generates test activation sequences on the heart and compares the corresponding test body surface potential maps (BSPMs) to the measured ones. The test excitation propagation patterns are constructed under the assumption of a global conduction velocity in the heart, which is violated in the cardiac resynchronization (CRT) patients suffering from conduction disturbances. In the present work, we propose to apply dynamic time warping (DTW) to the test and measured ECGs before measuring their similarity. The warping step is a non-linear pattern matching that compensates for local delays in the temporal sequences, thus accounting for the inhomogeneous excitation propagation, while aligning them in an optimal way with respect to a distance function. To evaluate benefits of the temporal warping for FRA-based BSPMs, we considered three scenarios. In the first setting, a simplified simulation example was constructed to illustrate the temporal warping and display the resulting distance map. Then, we applied the proposed method to eight BSPMs produced by realistic ectopic activation sequences and compared its performance to FRA. Finally, we assessed localization accuracy of both techniques in ten CRT patients. For each patient, we noninvasively imaged two paced ECGs: from left and right ventricular implanted leads. In all scenarios, FRA-DTW outperformed FRA in terms of LEs. For the clinical cases, the median (25–75% range) distance errors were reduced from 16 (8–23)mm to 5 (2–10)mm for all pacings, from 15 (11–25)mm to 8 (3–13)mm in the left, and from 19 (6–23)mm to 4 (2–8)mm in the right ventricle, respectively. The obtained results suggest the ability of temporal ECG warping to compensate for an inhomogeneous conduction profile, while retaining computational efficiency intrinsic to FRA.

Background: Up to 30–50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge.Objective: The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.Materials and Methods: Retrospective data from 57 patients who underwent CRT device implantation was utilized. Positive response to CRT was defined by a 10% increase in the left ventricular ejection fraction in a year after implantation. For each patient, an anatomical model of the heart and torso was reconstructed from MRI and CT images and tailored to ECG recorded in the participant. The models were used to compute ventricular activation time, ECG duration and electrical dyssynchrony indices during intrinsic rhythm and BiV pacing from the sites of implanted leads. For building a predictive model of CRT response, we used clinical data recorded before CRT device implantation together with model-derived biomarkers of ventricular excitation in the left bundle branch block mode of activation and under BiV stimulation. Several Machine Learning (ML) classifiers and feature selection algorithms were tested on the hybrid dataset, and the quality of predictors was assessed using the area under receiver operating curve (ROC AUC). The classifiers on the hybrid data were compared with ML models built on clinical data only.Results: The best ML classifier utilizing a hybrid set of clinical and model-driven data demonstrated ROC AUC of 0.82, an accuracy of 0.82, sensitivity of 0.85, and specificity of 0.78, improving quality over that of ML predictors built on clinical data from much larger datasets by more than 0.1. Distance from the LV pacing site to the post-infarction zone and ventricular activation characteristics under BiV pacing were shown as the most relevant model-driven features for CRT response classification.Conclusion: Our results suggest that combination of clinical and model-driven data increases the accuracy of classification models for CRT outcomes.

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