Kim Jin Woong, Seok Hyeon Seok, Shin Hangsik
Department of Biomedical Engineering, Chonnam National University, Yeosu-si, South Korea.
Front Physiol. 2021 Mar 30;12:596060. doi: 10.3389/fphys.2021.596060. eCollection 2021.
In mobile healthcare, heart rate variability (HRV) is increasingly being used in dynamic patient states. In this situation, shortening of the measurement time is required. This study aimed to validate ultra-short-term HRV in non-static conditions. We conducted electrocardiogram (ECG) measurements at rest, during exercise, and in the post-exercise recovery period in 30 subjects and analyzed ultra-short-term HRV in time and frequency domains by ECG in 10, 30, 60, 120, 180, and 240-s intervals, and compared the values to the 5-min HRV. For statistical analysis, null hypothesis testing, Cohen's statistics, Pearson's correlation coefficient, and Bland-Altman analysis were used, with a statistical significance level of < 0.05. The feasibility of ultra-short-term HRV and the minimum time required for analysis showed differences in each condition and for each analysis method. If the strict criteria satisfying all the statistical methods were followed, the ultra-short-term HRV could be derived from a from 30 to 240-s length of ECG. However, at least 120 s was required in the post-exercise recovery or exercise conditions, and even ultra-short-term HRV was not measurable in some variables. In contrast, according to the lenient criteria needed to satisfy only one of the statistical criteria, the minimum time required for ultra-short-term HRV analysis was 10-60 s in the resting condition, 10-180 s in the exercise condition, and 10-120 s in the post-exercise recovery condition. In conclusion, the results of this study showed that a longer measurement time was required for ultra-short-term HRV analysis in dynamic conditions. This suggests that the existing ultra-short-term HRV research results derived from the static condition cannot applied to the non-static conditions of daily life and that a criterion specific to the non-static conditions are necessary.
在移动医疗保健中，心率变异性（HRV）越来越多地用于动态患者状态评估。在这种情况下，需要缩短测量时间。本研究旨在验证非静态条件下的超短期HRV。我们对30名受试者在静息、运动期间和运动后恢复期进行了心电图（ECG）测量，并在10、30、60、120、180和240秒间隔内通过ECG分析了超短期HRV的时域和频域，并将这些值与5分钟HRV进行比较。对于统计分析，使用了零假设检验、科恩统计量、皮尔逊相关系数和布兰德-奥特曼分析，统计显著性水平<0.05。超短期HRV的可行性和分析所需的最短时间在每种条件和每种分析方法中均显示出差异。如果遵循满足所有统计方法的严格标准，超短期HRV可从30至240秒长度的ECG中得出。然而，在运动后恢复或运动条件下至少需要120秒，并且在某些变量中甚至无法测量超短期HRV。相比之下，根据仅满足一项统计标准所需的宽松标准，静息条件下超短期HRV分析所需的最短时间为10 - 60秒，运动条件下为10 - 180秒，运动后恢复条件下为10 - 120秒。总之，本研究结果表明，在动态条件下进行超短期HRV分析需要更长的测量时间。这表明从静态条件得出的现有超短期HRV研究结果不能应用于日常生活的非静态条件，并且需要特定于非静态条件的标准。
