Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography.

ABSTRACT:

Objective

Chest pain is common, and current risk-stratification methods, requiring 12-lead electrocardiograms (ECGs) and serial biomarker assays, are static and restricted to highly resourced settings. Our objective was to predict myocardial injury using continuous single-lead ECG waveforms similar to those obtained from wearable devices and to evaluate the potential of transfer learning from labeled 12-lead ECGs to improve these predictions.

Methods

We studied 10 874 Emergency Department (ED) patients who received continuous ECG monitoring and troponin testing from 2020 to 2021. We defined myocardial injury as newly elevated troponin in patients with chest pain or shortness of breath. We developed deep learning models of myocardial injury using continuous lead II ECG from b

SUBMITTER: Jin BT 

PROVIDER: S-EPMC9552286 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature