Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:139-142. doi: 10.1109/EMBC48229.2022.9871913.
During incremental exercise, two ventilatory thresholds (VT1, VT2) can normally be identified from gas exchange and ventilatory measurements, such as oxygen uptake, carbon dioxide production and ventilation. In this paper, we attempt to estimate the VT2 using HRV indices derived from a wearable electrocardiogram during a maximal exercise test. The exercise test is conducted on a treadmill that raises its speed by 0.5 km/h every minute. We have 42 measured exercise tests from 24 healthy male volunteers. Three experts determined the VT2 in each exercise test independently and we used principal component subspace reconstruction of their determinations to compute a collective VT2 for our machine learning model. The results demonstrate that the VT2 can be estimated from HRV using the proposed method with a reasonable performance during a maximal exercise test. In 28 out of 42 exercise tests, the HRV-derived threshold (HRVT) is within a minute (one phase) of the collective expert's determination.
在递增运动期间,通常可以从气体交换和通气测量中识别出两个通气阈值(VT1、VT2),例如摄氧量、二氧化碳产生量和通气量。在本文中,我们尝试使用可穿戴心电图的 HRV 指数来估计最大运动测试期间的 VT2。运动测试在跑步机上进行,跑步机每分钟以 0.5 公里/小时的速度提高速度。我们有 42 次来自 24 名健康男性志愿者的测量运动测试。三位专家在每项运动测试中独立确定 VT2,我们使用他们的测定值的主成分子空间重建来为我们的机器学习模型计算集体 VT2。结果表明,使用提出的方法可以从 HRV 中估计 VT2,并且在最大运动测试期间具有合理的性能。在 42 次运动测试中的 28 次中,HRV 衍生的阈值(HRVT)与专家集体测定值相差一分钟(一个阶段)。