Miura Kotaro, Goto Shinichi, Katsumata Yoshinori, Ikura Hidehiko, Shiraishi Yasuyuki, Sato Kazuki, Fukuda Keiichi
Department of Cardiology, Keio University School of Medicine, Tokyo, Japan.
Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan.
NPJ Digit Med. 2020 Oct 29;3:141. doi: 10.1038/s41746-020-00348-6. eCollection 2020.
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) and the DLT (DLT-VO). Our DL model showed that the DLT-VO was confirmed to be significantly correlated with the VT-VO ( = 0.875; < 0.001), and the mean difference was nonsignificant (-0.05 ml/kg/min, > 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.
规律的有氧体育活动对于维持良好的健康状况和预防心血管疾病（CVD）至关重要。尽管心肺运动试验（CPX）是无创评估通气阈值（VT）的重要检查，VT在临床上等同于有氧运动，但其评估需要昂贵的呼吸气体分析仪和专业知识。为了解决这些不便之处，本研究调查了使用单导联心电图（ECG）的深度学习（DL）算法估计有氧运动阈值的可行性。对连续260例接受CPX的CVD患者进行了分析。单导联ECG数据以1000Hz的采样率存储为时间序列电压数据。预处理后的ECG数据和通过呼吸气体分析仪计算出的VT时间点数据用于训练神经网络。将训练好的模型应用于独立测试队列，并计算DL阈值（DLT；通过DL算法估计的VT时间）。我们比较了VT时的摄氧量（VT-VO）与DLT（DLT-VO）之间的相关性。我们的DL模型显示，DLT-VO与VT-VO显著相关（r = 0.875；P < 0.001），平均差异无统计学意义（-0.05 ml/kg/min，P > 0.05），这表明VT与DLT之间具有很强的一致性。使用单导联ECG数据的DL算法能够准确估计CVD患者的VT。DL算法可能是估计有氧运动阈值的一种新方法。
