We have previously developed an automated localization method based on multiple linear regression (MLR) model to estimate the activation origin on a generic left-ventricular (LV) endocardial surface in real time from the 12-lead ECG. The present study sought to investigate whether machine learning-namely, random-forest regression (RFR) and support-vector regression (SVR)-can improve the localization accuracy compared to MLR. For 38 patients the 12-lead ECG was acquired during LV endocardial pacing at 1012 sites with known coordinates exported from an electroanatomic mapping system; each pacing site was then registered to a generic LV endocardial surface subdivided into 16 segments tessellated into 238 triangles. ECGs were reduced to one variable per lead, consisting of 120-ms time integral of the QRS. To compare three regression models, the entire dataset ([Formula: see text]) was partitioned at random into a design set with 80% and a test set with the remaining 20% of the entire set, and the localization error-measured as geodesic distance on the generic LV surface-was assessed. Bootstrap method with replacement, using 1000 resampling trials, estimated each model's error distribution for the left-out sample ([Formula: see text]). In the design set ([Formula: see text]), the mean accuracy was 8.8, 12.1, and 12.9 mm, respectively for SVR, RVR and MLR. In the test set ([Formula: see text]), the mean value of the localization error in the SVR model was consistently lower than the other two models, both in comparison with the MLR (11.4 vs. 12.5 mm), and with the RFR (11.4 vs. 12.0 mm); the RFR model was also better than the MLR model for estimating localization accuracy (12.0 vs. 12.5 mm). The bootstrap method with 1,000 trials confirmed that the SVR and RFR models had significantly higher predictive accurate than the MLR in the bootstrap assessment with the left-out sample (SVR vs. MLR ([Formula: see text]), RFR vs. MLR ([Formula: see text])). The performance comparison of regression models showed that a modest improvement in localization accuracy can be achieved by SVR and RFR models, in comparison with MLR. The "population" coefficients generated by the optimized SVR model from our dataset are superior to the previously-derived "population" coefficients generated by the MLR model and can supersede them to improve the localization of ventricular activation on the generic LV endocardial surface.
Keywords: 12-Lead ECG; Catheter ablation; Multiple linear regression; Pace-mapping; Random forest regression; Support vector regression; Ventricular tachycardia.