Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128, Palermo, Italy.
Department of Physiology and the Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia.
Med Biol Eng Comput. 2019 Jun;57(6):1247-1263. doi: 10.1007/s11517-019-01957-4. Epub 2019 Feb 7.
Heart rate variability (HRV) analysis represents an important tool for the characterization of complex cardiovascular control. HRV indexes are usually calculated from electrocardiographic (ECG) recordings after measuring the time duration between consecutive R peaks, and this is considered the gold standard. An alternative method consists of assessing the pulse rate variability (PRV) from signals acquired through photoplethysmography, a technique also employed for the continuous noninvasive monitoring of blood pressure. In this work, we carry out a thorough analysis and comparison of short-term variability indexes computed from HRV time series obtained from the ECG and from PRV time series obtained from continuous blood pressure (CBP) signals, in order to evaluate the reliability of using CBP-based recordings in place of standard ECG tracks. The analysis has been carried out on short time series (300 beats) of HRV and PRV in 76 subjects studied in different conditions: resting in the supine position, postural stress during 45° head-up tilt, and mental stress during computation of arithmetic test. Nine different indexes have been taken into account, computed in the time domain (mean, variance, root mean square of the successive differences), frequency domain (low-to-high frequency power ratio LF/HF, HF spectral power, and central frequency), and information domain (entropy, conditional entropy, self entropy). Thorough validation has been performed using comparison of the HRV and PRV distributions, robust linear regression, and Bland-Altman plots. Results demonstrate the feasibility of extracting HRV indexes from CBP-based data, showing an overall relatively good agreement of time-, frequency-, and information-domain measures. The agreement decreased during postural and mental arithmetic stress, especially with regard to band-power ratio, conditional, and self-entropy. This finding suggests to use caution in adopting PRV as a surrogate of HRV during stress conditions.
心率变异性（HRV）分析是描述复杂心血管控制的重要工具。HRV 指数通常通过测量连续 R 波峰值之间的时间间隔，从心电图（ECG）记录中计算得出，这被认为是金标准。另一种方法是通过光体积描记法评估脉搏率变异性（PRV），该技术也用于连续无创血压监测。在这项工作中，我们对从 ECG 获得的 HRV 时间序列和从连续血压（CBP）信号获得的 PRV 时间序列计算的短期变异性指数进行了全面分析和比较，以评估使用基于 CBP 的记录代替标准 ECG 轨道的可靠性。该分析在 76 名不同条件下研究的受试者的 HRV 和 PRV 短时间序列（300 次心跳）上进行：仰卧位休息、45°头高位姿势应激和心算时的精神应激。共考虑了 9 个不同的指数，在时域（均值、方差、连续差异的均方根）、频域（低到高频功率比 LF/HF、HF 频谱功率和中心频率）和信息域（熵、条件熵、自熵）中进行计算。通过比较 HRV 和 PRV 分布、稳健线性回归和 Bland-Altman 图进行了彻底验证。结果表明，从基于 CBP 的数据中提取 HRV 指数是可行的，表明时间、频率和信息域测量的整体相对较好的一致性。在姿势和心算应激期间，一致性降低，特别是在带功率比、条件和自熵方面。这一发现表明，在应激条件下，应谨慎采用 PRV 作为 HRV 的替代指标。
