Optimization of A 12-lead ECG subset for Automated Early Left-Ventricular Activation Localization Approach based on Pace-Mapping Technology.

Abstract

We previously developed an automated approach based on pace-mapping to localize early left-ventricular (LV) activation origin. To avoid a singular system, we require pacing from at least 2 more known sites than the number of ECG leads used. Fewer leads used means fewer pacing sites required. To identify an optimal minimal ECG-lead set for the automated approach. We used 1715 LV endocardial pacing sites to create derivation and testing datasets. The derivation dataset, consisting of 1012 known pacing sites pooled from 38 patients, was used to identify an optimal 3-lead set using random-forest regression (RFR), and a second 3-lead set using exhaustive search. The performance of these sets and the calculated Frank leads was compared within the testing dataset with 703 pacing sites pooled from 25 patients. The RFR yielded III, V1, and V4, while the exhaustive search identified leads II, V2 and V6. Comparison of these sets and the calculated Frank leads demonstrated similar performance when utilizing ≥ 5 known pacing sites. Accuracy improved with additional pacing sites, achieving mean accuracy of < 5 mm, after including up to 9 pacing sites when they were focused on a suspect area of ventricular activation origin (radius < 10 mm). The RFR identified the quasi-orthogonal leads set to localize the source of LV activation, minimizing the training set of pacing sites. Localization accuracy was high using these leads, and was not significantly different from using leads identified by exhaustive search, or empiric use of Frank leads.