Robert D. Anderson

BACKGROUND: Accurate delineation of scar patterns is valuable for guiding catheter ablation of ventricular tachycardia. We hypothesized that scar and its pattern of distribution can be determined from intracardiac electrograms using computational signal processing and that further improvements in classification can be achieved with a convolutional neural network. METHODS: A total of 5 sheep underwent anteroseptal infarction (plus 1 healthy control) with electroanatomic mapping (129±12 days post-infarct). A whole-heart histological model of the postinfarction scar was created and coregistered to ventricular electrograms. Electrograms were matched to scar pattern categories; no scar, at least endocardial scar: at least intramural scar (intramural scar sparing the endocardium), or epicardial-only scar (epicardial scar sparing the endocardium/intramural space). A suite of signal-processing features was extracted from bipolar electrograms. Furthermore, bipolar and unipolar electrograms were used to train a time series convolutional neural network (InceptionTime). RESULTS: A total of 11 551 electrograms were matched to 451 biopsies. Bipolar and unipolar voltage alone were poor classifiers of scar patterns. For each of the scar labels, 20 bipolar electrogram features (predominantly within the frequency domain) yielded an area under the curve of 0.815, 0.810, 0.704, and 0.681 to predict no scar, at least endocardial scar, at least intramural scar, and epicardial-only scar, respectively. Substantial improvement was achieved with a convolutional neural network trained on unipolar electrograms: areas under the curve and accuracy (averaged across wavefronts) were 0.977 and 0.929 for no scar, 0.970 and 0.919 for at least endocardial scar, 0.909 and 0.959 for at least intramural scar and 0.926 and 0.958 for epicardial-only scar. CONCLUSIONS: Convolutional neural network-derived analysis of unipolar electrogram data has excellent predictive value for determination of scar patterns. Computational analyses of electrogram data beyond voltage and other time-domain features are necessary to improve the identification of arrhythmogenic sites in the ventricle.

BACKGROUND: Cardiac magnetic resonance imaging (CMR)–defined ventricular scar and anatomic conduction channels (CMR-CCs) offer promise in delineating ventricular tachycardia substrate. No studies have validated channels with coregistered histology, nor have they ascertained the histological characteristics of deceleration zones (DZs) within these channels. We aimed to validate CMR scar and CMR-CCs with whole-heart histology and electroanatomic mapping in a postinfarction model. METHODS: Five sheep underwent anteroseptal infarction. CMR (116±20 days post infarct) was postprocessed using ADAS-3D, varying pixel intensity thresholds (5545, 6040, 6535, and 7030). DZs were identified by electroanatomic mapping (129±12 days post infarct). Explanted hearts were sectioned and stained with Picrosirius red, and whole-heart histopathologic shells were generated. Scar topography as well as percentage fibrosis, adiposity, and remaining viable myocardium within 3 mm histological biopsies and within CMR-CCs were determined. RESULTS: Using the standard 6040 thresholding, CMR had 83.8% accuracy for identifying histological scar in the endocardium (κ, 0.666) and 61.4% in the epicardium (κ, 0.276). Thirty-seven CMR-CCs were identified by varying thresholding; 23 (62%) were unique. DZs colocalized to 19 of 23 (83%) CMR-CCs. Twenty (87%) CMR-CCs were histologically confirmed. Within-channel histological fibrosis did not differ by the presence of DZs ( P =0.242). Within-channel histological adiposity was significantly higher at sites with versus without DZs (24.1% versus 8.3%; P <0.001). CONCLUSIONS: Postprocessed CMR-derived scars and channels were validated by histology and electroanatomic mapping. Regions of CMR-CCs at sites of DZs had higher adiposity but similar fibrosis than regions without DZs, suggesting that lipomatous metaplasia may contribute to arrhythmogenicity of postinfarction scar.

Background: Conventional mapping of focal ventricular arrhythmias relies on unipolar electrogram characteristics and early local activation times. Deep intramural foci are common and associated with high recurrence rates following catheter-based radiofrequency ablation. We assessed the accuracy of unipolar morphological patterns and mapping surface indices to predict the site and depth of ventricular arrhythmogenic focal sources. Methods: An experimental beating-heart model used Langendorff-perfused, healthy swine hearts. A custom 56-pole electrode array catheter was positioned on the left ventricle. A plunge needle was placed perpendicular in the center of the grid to simulate arrhythmic foci at variable depths. Unipolar electrograms and local activation times were generated. Simulation models from 2 human hearts were also included with grids positioned simultaneously on the endocardium-epicardium from multiple left ventricular, septal, and outflow tract sites. Results: A unipolar Q or QS complex lacks specificity for superficial arrhythmic foci, as this morphology pattern occupies a large surface area and is the predominant pattern as intramural depth increases without developing a R component. There is progressive displacement from the arrhythmic focus to the surface exit as intramural focus depth increases. A shorter total activation time over the overlying electrode array, larger surface area within initial 20 ms activation, and a dual surface breakout pattern all indicate a deep focus. Conclusions: Displacement from the focal intramural origin to the exit site on the mapping surface could lead to erroneous lesion delivery strategies. Traditional unipolar electrogram features lack specificity to predict the intramural arrhythmic source; however, novel endocardial-epicardial mapping surface indices can be used to determine the depth of arrhythmic foci.