Deep learning on resting electrocardiogram to identify impaired heart rate recovery

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.We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRRAmong 56,793 individuals (mean age 57 years, 51% women), the HRRDeep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study.