Deep learning on resting electrocardiogram to identify impaired heart rate recovery.

ABSTRACT:

Background and objective

Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR.

Methods

We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRR_pred) among UK Biobank participants who had undergone exercise testing. We examined the association of HRR_pred with incident cardiovascular disease using Cox models, and investigated the genetic architecture of HRR_pred in genome-wide association analysis.

Results

Among 56,793 individuals (mean age 57 years, 51% women), the HRR

SUBMITTER: Diamant N 

PROVIDER: S-EPMC9422063 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature