Huang Shu-Chun, Lee Chen-Hung, Hsu Chih-Chin, Chang Sing-Ya, Chen Yu-An, Chiu Chien-Hung, Hsiao Ching-Chung, Su Hong-Ren
Department of Physical Medicine and Rehabilitation, New Taipei Municipal Tucheng Hospital, Chang Gung Memorial Hospital, Taipei, Taiwan.
Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taiwan.
Front Physiol. 2023 Oct 25;14:1253598. doi: 10.3389/fphys.2023.1253598. eCollection 2023.
The acquisition of blood lactate concentration (BLC) during exercise is beneficial for endurance training, yet a convenient method to measure it remains unavailable. BLC and electrocardiogram (ECG) both exhibit variations with changes in exercise intensity and duration. In this study, we hypothesized that BLC during exercise can be predicted using ECG data. Thirty-one healthy participants underwent four cardiopulmonary exercise tests, including one incremental test and three constant work rate (CWR) tests at low, moderate, and high intensity. Venous blood samples were obtained immediately after each CWR test to measure BLC. A mathematical model was constructed using 31 trios of CWR tests, which utilized a residual network combined with long short-term memory to analyze every beat of lead II ECG waveform as 2D images. An artificial neural network was used to analyze variables such as the RR interval, age, sex, and body mass index. The standard deviation of the fitting error was 0.12 mmol/L for low and moderate intensities, and 0.19 mmol/L for high intensity. Weighting analysis demonstrated that ECG data, including every beat of ECG waveform and RR interval, contribute predominantly. By employing 2D convolution and artificial neural network-based methods, BLC during exercise can be accurately estimated non-invasively using ECG data, which has potential applications in exercise training.
运动过程中获取血乳酸浓度（BLC）对耐力训练有益，但仍缺乏一种便捷的测量方法。BLC和心电图（ECG）都会随运动强度和持续时间的变化而变化。在本研究中，我们假设运动期间的BLC可以通过ECG数据进行预测。31名健康参与者进行了四项心肺运动测试，包括一项递增测试和三项低、中、高强度的恒定工作率（CWR）测试。在每次CWR测试后立即采集静脉血样以测量BLC。使用31组CWR测试构建了一个数学模型，该模型利用残差网络结合长短期记忆，将II导联ECG波形的每一个搏动作为二维图像进行分析。使用人工神经网络分析RR间期、年龄、性别和体重指数等变量。低强度和中等强度时拟合误差的标准差为0.12 mmol/L，高强度时为0.19 mmol/L。权重分析表明，包括ECG波形的每一个搏动和RR间期在内的ECG数据起主要作用。通过采用基于二维卷积和人工神经网络的方法，可以利用ECG数据非侵入性地准确估计运动期间的BLC，这在运动训练中具有潜在应用价值。
