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Regular aerobic physical activity is of utmost importance in maintaining a good health status and preventing cardiovascular diseases (CVDs). Although cardiopulmonary exercise testing (CPX) is an essential examination for noninvasive estimation of ventilatory threshold (VT), defined as the clinically equivalent to aerobic exercise, its evaluation requires an expensive respiratory gas analyzer and expertize. To address these inconveniences, this study investigated the feasibility of a deep learning (DL) algorithm with single-lead electrocardiography (ECG) for estimating the aerobic exercise threshold. Two hundred sixty consecutive patients with CVDs who underwent CPX were analyzed. Single-lead ECG data were stored as time-series voltage data with a sampling rate of 1000 Hz. The data of preprocessed ECG and time point at VT calculated by respiratory gas analyzer were used to train a neural network. The trained model was applied on an independent test cohort, and the DL threshold (DLT; a time of VT estimated through the DL algorithm) was calculated. We compared the correlation between oxygen uptake of the VT (VT–VO 2 ) and the DLT (DLT–VO 2 ). Our DL model showed that the DLT–VO 2  was confirmed to be significantly correlated with the VT–VO 2  ( r  = 0.875;  P  < 0.001), and the mean difference was nonsignificant (−0.05 ml/kg/min,  P  > 0.05), which displayed strong agreements between the VT and the DLT. The DL algorithm using single-lead ECG data enabled accurate estimation of VT in patients with CVDs. The DL algorithm may be a novel way for estimating aerobic exercise threshold.

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data