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Background We have previously developed an intraprocedural automatic arrhythmia‐origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3‐lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers. The AAOL system combines 120‐ms QRS integrals of 3 leads (III, V2, V6) with pace mapping to predict VT exit/PVC origin site and projects that site onto the patient‐specific electroanatomic mapping surface. VT exit/PVC origin sites were clinically identified by activation mapping and/or pace mapping. The localization error of the VT exit/PVC origin site was assessed by the distance between the clinically identified site and the estimated site. In the retrospective study of 19 patients with structural heart disease, the AAOL system achieved a mean localization accuracy of 6.5±2.6 mm for 25 induced VTs. In the prospective study with 23 patients, mean localization accuracy was 5.9±2.6 mm for 26 VT exit and PVC origin sites. There was no difference in mean localization error in epicardial sites compared with endocardial sites using the AAOL system (6.0 versus 5.8 mm, P =0.895). Conclusions The AAOL system achieved accurate localization of VT exit/PVC origin sites in patients with structural heart disease; its performance is superior to current systems, and thus, it promises to have potential clinical utility.

BACKGROUND: We previously developed an intraprocedural automated site of origin localization system to identify the origin of early left ventricular (LV) activation using 12-lead ECGs. However, it has limitations, as it could not identify the site of origin in the right ventricle (RV), and relied on acquiring a complete electroanatomic map (EAM). OBJECTIVE: The objective of this study was to present a new system, the Automatic Arrhythmia Origin Localization (AAOL) system, which utilized incomplete EAM for localization of idiopathic ventricular arrhythmia (IVA) origin on the patient-specific geometry of LV, RV and neighboring vessels. The accuracy of the system in localizing IVA source sites on cardiac structures where pace-mapping is challenging was assessed. METHODS: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The new system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with pace mapping to predict the site of earliest ventricular activation. The predicted site was projected onto EAM geometry. RESULTS: Twenty-three IVA origin sites were clinically identified by activation mapping and/or pace mapping (8 RV; 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs. CONCLUSIONS: The new intraprocedural AAOL system achieved accurate localization of IVA origin in ventricles and neighbouring vessels, which could facilitate ablation procedures for patients with IVAs. Key Words: Idiopathic ventricular arrhythmias (IVA); Premature ventricular complexes (PVCs); idiopathic ventricular tachycardia (IVT); Pace mapping; Activation mapping; Radiofrequency (RF) Ablation; ECG.

Introduction: Few intraprocedural localization systems have been developed to predict idiopathic ventricular arrhythmia (IVA) source sites. However, an accurate and bi-ventricular patient-specific automated site of origin localization system remains elusive. To address this issue, we have developed a new automatic arrhythmia origin localization (AAOL) system that determines the sites of earliest activation in both ventricles and provides superior accuracy. Hypothesis: We hypothesized that the AAOL system can use electroanatomic mapping (EAM) geometry and accurately localize IVA source sites on patient-specific geometry of LV, RV and neighboring vessels using 3-lead ECGs. Methods: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The AAOL system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with intracardiac pacing to predict the site of earliest ventricular activation. The predicted site was projected onto the EAM geometry using the EAM triangular-mesh site nearest to the tip of the predicted site. Results: Twenty-three IVA source sites were clinically identified by activation mapping and/or pace mapping (8 RV, 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs (Figure 1D), better than that achieved by previous systems. Conclusions: The new AAOL system offers highly accurate localization of IVA source sites in both ventricles and neighboring vessels, which could facilitate ablation procedures for patients with IVAs.

Background: To facilitate ablation of ventricular tachycardia (VT), an automated localization system to identify the site of origin of left ventricular activation in real time using the 12-lead ECG was developed. The objective of this study was to prospectively assess its accuracy. Methods: The automated site of origin localization system consists of 3 steps: (1) localization of ventricular segment based on population templates, (2) population-based localization within a segment, and (3) patient-specific site localization. Localization error was assessed by the distance between the known reference site and the estimated site. Results: In 19 patients undergoing 21 catheter ablation procedures of scar-related VT, site of origin localization accuracy was estimated using 552 left ventricular endocardial pacing sites pooled together and 25 VT-exit sites identified by contact mapping. For the 25 VT-exit sites, localization error of the population-based localization steps was within 10 mm. Patient-specific site localization achieved accuracy of within 3.5 mm after including up to 11 pacing (training) sites. Using 3 remotes (67.8±17.0 mm from the reference VT-exit site), and then 5 close pacing sites, resulted in localization error of 7.2±4.1 mm for the 25 identified VT-exit sites. In 2 emulated clinical procedure with 2 induced VTs, the site of origin localization system achieved accuracy within 4 mm. Conclusions: In this prospective validation study, the automated localization system achieved estimated accuracy within 10 mm and could thus provide clinical utility.

Objectives The aim of this study was to develop rapid computational methods for identifying the site of origin of ventricular activation from the 12-lead electrocardiogram. Background Catheter ablation of ventricular tachycardia in patients with structural heart disease frequently relies on a substrate-based approach, which may use pace mapping guided by body-surface electrocardiography to identify culprit exit sites. Methods Patients undergoing ablation of scar-related VT (n = 38) had 12-lead electrocardiograms recorded during pacing at left ventricular endocardial sites (n = 1,012) identified on 3-dimensional electroanatomic maps and registered to a generic left ventricular endocardial surface divided into 16 segments and tessellated into 238 triangles; electrocardiographic data were reduced for each lead to 1 variable, consisting of QRS time integral. Two methods for estimating the origin of activation were developed: 1) a discrete method, estimating segment of activation origin using template matching; and 2) a continuous method, using population-based multiple linear regression to estimate triangle of activation origin. A variant of the latter method was derived, using patient-specific multiple linear regression. Results The optimal QRS time integral included the first 120 ms of the QRS interval. The mean localization error of population-based regressions was 12 ± 8 mm. Patient-specific regressions can achieve localization accuracy better than 5 mm when at least 10 training-set pacing sites are used; this accuracy further increases with each added pacing site. Conclusions Computational intraprocedure methods can automatically identify the segment and site of left ventricular activation using novel algorithms, with accuracy within