A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms

Ryan Missel, Prashnna K. Gyawali, Jaideep Vitthal Murkute, Zhiyuan Li, Shijie Zhou, Amir AbdelWahab, Jason Davis, James Warren, John L. Sapp, Linwei Wang

Abstract: Highlights • A novel hybrid model combining the advantage of population-based deep learning and patient-specific model. • A novel population model that disentangles inter-subject variations when localizing the sites of origins. • A novel patient-specific model that actively suggests where to pace in order to minimize the number of pacing training data. Abstract Background Machine learning models may help localize the site of origin of ventricular tachycardia (VT) using 12-lead electrocardiograms. However, population-based models suffer from inter-subject anatomical variations within ECG data, while patient-specific models face the open challenge of what pacing data to collect for training. Methods This study presents and validates the first hybrid model that combines population and patient-specific machine learning for rapid “computer-guided pace-mapping”. A population-based deep learning model was first trained offline to disentangle inter-subject variations and regionalize the site of VT origin. Given a new patient with a target VT, an on-line patient-specific model -- after being initialized by the population-based prediction -- was then built in real time by actively suggesting where to pace next and improving the prediction with each added pacing data, progressively guiding pace-mapping towards the site of VT origin. Results The population model was trained on pace-mapping data from 38 patients and the patient-specific model was subsequently tuned on one patient. The resulting hybrid model was tested on a separate cohort of eight patients in localizing 1) 193 LV endocardial pacing sites, and 2) nine VTs with clinically determined exit sites. The hybrid model achieved a localization error of 5.3 ± 2.6 mm using 5.4 ± 2.5 pacing sites in localizing LV pacing sites, achieving a significantly higher accuracy with a significantly smaller amount of training sites in comparison to models without active guidance. Conclusion The presented hybrid model has the potential to assist rapid pace-mapping of interventional targets in VT.