Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Computers in biology and medicine

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.

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

2020-Sep-23

Active learning, Disentangled representation learning, Electrocardiogram, Pace-mapping, Ventricular tachycardia