Machine-learning-based identification of arrhythmia-driving regions in ventricular tachycardia
Using neighborhood-aggregated multidomain features from 3-D electroanatomical mapping
Publication date
2026-08-31
Document type
Conference slides
Other contributor
Obergassel, Julius
Organisational unit
Conference
48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026) ; Toronto, Canada ; 26–30 July 2026
Publisher
Universitätsbibliothek der HSU/UniBw H
Part of the university bibliography
✅
Language
English
Abstract
Electroanatomical 3-D mapping provides bipolar electrograms (EGMs) to guide ablation for scar-related ventricular tachycardia (VT). However, identifying arrhythmia-driving regions remains challenging and operator-dependent. We propose and evaluate a supervised machine-learning pipeline that combines point-wise EGM features with explicit 3-D neighborhood aggregation to prioritize potential ablation target regions. We retrospectively analyzed 86,765 bipolar EGMs from 14 VT patients and constructed a curated dataset of 16,078 EGMs, including 1,729 EGMs from clinically defined arrhythmogenic (ARR) regions. We compared three feature sets: point-wise baseline features (FS 1), baseline plus 5-mm neighborhood aggregation (FS 2), and a compact feature subset (FS 3). Three classifiers (sup- port vector machine, k-nearest neighbors, and bagged trees) were evaluated using repeated stratified cross-validation on the curated dataset and leave-one-patient-out (LOPO) full-map validation. In repeated cross-validation, the best FS 1 model achieved 62.1% sensitivity, 98.8% specificity, and 92.8% accuracy. Using FS 2 improved performance to 92.6% sensitivity, 99.6% specificity, and 98.5% accuracy. In LOPO full-map validation, specificity remained high (97.5%), whereas sensitivity decreased under realistic class imbalance. These results suggest that explicit 3-D neighborhood context improves the discrimination between ARR and NON-ARR regions and may support prioritizing potential ablation target regions in VT.
Description
This presentation is associated with the conference paper presented orally at the IEEE Engineering in Medicine and Biology Conference (EMBC) 2026. It summarizes the study on machine-learning-based identification of arrhythmia-driving regions in ventricular tachycardia using neighborhood-aggregated multidomain features from 3-D electroanatomical mapping. The corresponding paper is scheduled for publication in the EMBC 2026 conference proceedings.
Version
Author's original
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