Abstract
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–VO2) and the DLT (DLT–VO2). Our DL model showed that the DLT–VO2 was confirmed to be significantly correlated with the VT–VO2 (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.
Highlights
Adequate regular physical activity is paramount to maintaining good health[1,2] and preventing cardiovascular diseases (CVDs)[3,4]
We found that the deep learning (DL) algorithm constructed with neural networks from single-lead ECG data during exercise enabled estimation of the ventilatory threshold (VT) in patients with CVDs
This study is unique in that we focused on the fact that electrical activity of the heart is dynamic during exercise and that the changes derived from hidden big data might resemble the feature of the VT
Summary
Adequate regular physical activity is paramount to maintaining good health[1,2] and preventing cardiovascular diseases (CVDs)[3,4]. Cardiopulmonary exercise testing (CPX) is an essential examination for noninvasively detecting the ventilatory threshold (VT), that is, defined as clinically equivalent to aerobic exercise, its assessment requires an expensive respiratory gas analyzer and expertize In this context, expansion of the exercise therapy with a simple, versatile methodology to facilitate its introduction and persistency is warranted to improve clinical outcomes of patients with CVDs. Advancement of high-performance computer technology and deep learning (DL) technology has enabled generation of models that accurately predict outcomes, detect diseases, and automatically classify or quantitate measurements from various modalities including electrophysiological and imaging data[9] (e.g., electrocardiography [ECG]10, echocardiography[11], computed tomography[12], single-photon emission computed tomography[13], and magnetic resonance imaging14) in cardiovascular medicine.
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