Discriminating electrocardiographic responses to His-bundle pacing using machine learning.

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 d

SUBMITTER: Arnold AD 

PROVIDER: S-EPMC7484933 | biostudies-literature | 2020 Jul-Aug

REPOSITORIES: biostudies-literature