Improving localization accuracy for non-invasive automated early left ventricular origin localization approach.

Background: We previously developed a non-invasive approach to localize the site of early left ventricular activation origin in real time using 12-lead ECG, and to project the predicted site onto a generic LV endocardial surface using the smallest angle between two vectors algorithm (SA). Objectives: To improve the localization accuracy of the non-invasive approach by utilizing the K-nearest neighbors algorithm (KNN) to reduce projection errors. Methods: Two datasets were used. Dataset #1 had 1012 LV endocardial pacing sites with known coordinates on the generic LV surface and corresponding ECGs, while dataset #2 included 25 clinically-identified VT exit sites and corresponding ECGs. The non-invasive approach used "population" regression coefficients to predict the target coordinates of a pacing site or VT exit site from the initial 120-m QRS integrals of the pacing site/VT ECG. The predicted site coordinates were then projected onto the generic LV surface using either the KNN or SA projection algorithm. Results: The non-invasive approach using the KNN had a significantly lower mean localization error than the SA in both dataset #1 (9.4 vs. 12.5 mm, p < 0.05) and dataset #2 (7.2 vs. 9.5 mm, p < 0.05). The bootstrap method with 1,000 trials confirmed that using KNN had significantly higher predictive accuracy than using the SA in the bootstrap assessment with the left-out sample (p < 0.05). Conclusion: The KNN significantly reduces the projection error and improves the localization accuracy of the non-invasive approach, which shows promise as a tool to identify the site of origin of ventricular arrhythmia in non-invasive clinical modalities.

Assessment of an ECG-Based System for Localizing Ventricular Arrhythmias in Patients With Structural Heart Disease.

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.

Prospective Multicenter Assessment of a New Intraprocedural Automated System for Localizing Idiopathic Ventricular Arrhythmia Origins.

OBJECTIVES: The objective of this study was to present a new system, the Automatic Arrhythmia Origin Localization (AAOL) system, which used incomplete electroanatomic mapping (EAM) for localization of idiopathic ventricular arrhythmia (IVA) origin on the patient-specific geometry of left ventricular, right ventricular, and neighboring vessels. The study assessed the accuracy of the system in localizing IVA source sites on cardiac structures where pace mapping is challenging. BACKGROUND: An intraprocedural automated site of origin localization system was previously developed to identify the origin of early left ventricular activation by using 12-lead electrocardiograms (ECGs). However, it has limitations, as it could not identify the site of origin in the right ventricle and relied on acquiring a complete EAM. 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, and 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, right ventricle; 15, left ventricle, 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 neighboring vessels, which could facilitate ablation procedures for patients with IVAs.

Prospective Assessment of an Automated Intraprocedural 12-Lead ECG-Based System for Localization of Early Left Ventricular Activation.

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.

Automated intraprocedural localization of origin of ventricular activation using patient-specific computed tomographic imaging.

BACKGROUND: To facilitate catheter ablation of ventricular tachycardia (VT), we previously developed an automated method to identify sources of left ventricular (LV) activation in real time using 12-lead electrocardiography (ECG), the accuracy of which depends on acquisition of a complete electroanatomic (EA) map. OBJECTIVE: The purpose of this study was to assess the feasibility of using a registered cardiac computed tomogram (CT) rather than an EA map to permit real-time localization and avoid errors introduced by incomplete maps. METHODS: Before LV VT ablation, 10 patients underwent CT imaging and 3-dimensional reconstruction of the cardiac surface to create a triangle mesh surface, which was registered to the EA map during the procedure and imported into custom localization software. The software uses QRS integrals from leads III, V2, and V6; derives personalized regression coefficients from pacing at ≥5 sites with known locations; and estimates the location of unknown activation sites on the 3-dimensional patient-specific LV endocardial surface. Localization accuracy was quantified for VT exit sites in millimeters by comparing the calculated against the known locations. RESULTS: The VT exit site was identified for 20 VTs using activation and entrainment mapping, supplemented by pace-mapping at the scar margin. The automated localization software achieved incremental accuracy with additional pacing sites and had a mean localization error of 6.9 ± 5.7 mm for the 20 VTs. CONCLUSION: Patient-specific CT geometry is feasible for use in real-time automated localization of ventricular activation and may avoid reliance on a complete EA map.

Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram.

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 <10 mm.