1. Discriminating electrocardiographic responses to His-bundle pacing using machine learning
- Author
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Ahran D. Arnold, MBBS, James P. Howard, MB BChir, Aiswarya Gopi, BSc, Cheng Pou Chan, BSc, Nadine Ali, BMBCh, Daniel Keene, MBChB, PhD, Matthew J. Shun-Shin, BMBCh, PhD, Yousif Ahmad, BMBS, PhD, Ian J. Wright, BSc, Fu Siong Ng, MBBS, PhD, Nick W.F. Linton, MBBS, PhD, Prapa Kanagaratnam, MB BChir, PhD, Nicholas S. Peters, MBBS, MD, FHRS, Daniel Rueckert, PhD, Darrel P. Francis, MB BChir, MD, and Zachary I. Whinnett, BMBS, PhD
- Subjects
Artificial intelligence ,Conduction system pacing ,Electrocardiography ,His-bundle pacing ,Machine learning ,Neural networks ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 ,Medical technology ,R855-855.5 - Abstract
Background: His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. Objective: The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. Methods: We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. Results: The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network’s performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P
- Published
- 2020
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