Cui Xingran, Tian Leirong, Li Zhengwen, Ren Zikai, Zha Keyang, Wei Xinruo, Peng Chung-Kang
Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, China.
Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou 215000, China.
Entropy (Basel). 2020 Nov 15;22(11):1302. doi: 10.3390/e22111302.
Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences ( < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).
心率变异性(HRV)已被广泛用作自主神经调节的指标,包括基于线性分析、熵以及多尺度熵的非线性分析。然而,它会受到信号记录条件的强烈影响。为了研究不同环境下健康HRV的变异性,我们使用可穿戴单导联心电图设备,在校园内记录了56名健康年轻大学生的心电图(ECG)信号(每位参与者记录20小时)。通过结合五种常用R波检测算法的优点来提取准确的R波到R波(RR)间期,以消除数据质量的影响。进行了全面而详细的线性和非线性HRV分析。从五个类别评估了HRV指标的变异性:(1)日常活动的不同状态;(2)自由日常活动期间同一天的不同记录时间段;(3)坐姿和躺姿;(4)左侧卧、右侧卧和仰卧;(5)性别影响。对于大多数分析的HRV指标,除了不同卧位外,在这五个类别中的不同记录条件之间发现了显著差异(<0.05)。结果表明,在HRV相关研究中应实施心电图数据收集和HRV分析的标准化,特别是对于基于熵和多尺度熵的分析。此外,这项初步研究提供了健康年轻受试者在各种记录条件下的HRV指标参考值,这些值可能对不同应用(如健康监测和管理)有用。