Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men
Dynamic heart rate variability: a tool for exploring sympathovagal balance continuously during sleep in men
复制标题
DOI：
10.1152/ajpheart.1998.275.3.h946
复制
发表时间：
1998-09-01
影响因子：
4.8
通讯作者：
Brandenberger, G
中科院分区：
文献类型：
--
作者：
Otzenberger, H;Gronfier, C;Brandenberger, G
我们最近证明了以1分钟间隔计算的心搏间R-R间期自相关系数（r（RR））的夜间曲线与反映睡眠深度的睡眠脑电图（EEG）平均频率的变化有关。庞加莱图的其他定量测量，即，正常R-R间期的标准差（SDNN）和连续R-R正常间期之间的均方根差（RMSSD）通常用于评估心率变异性。本研究旨在比较夜间的r（RR），SDNN和RMSSD与R-R频谱功率成分：高频（HF）功率，反映副交感神经活动;低频（LF）功率，反映交感神经活动与副交感神经成分的优势; LF-HF比（LF/HF），被视为交感迷走神经平衡的指标。对15名健康志愿者在睡眠期间每5分钟计算一次r（RR）、SDNN、RMSSD和频谱功率分量。r（RR）和LF/HF的过夜曲线呈坐标变化，相关系数非常显著（所有受试者P < 0.001）。SDNN与LF功率相关（P < 0.001），RMSSD与HF功率相关（P < 0.001）。夜间r（RR）曲线与EEG平均频率曲线呈高度相关（P < 0.001）。SDNN和EEG平均频率也高度交叉相关（除1例外，所有受试者P < 0.001）。RMSSD与EEG平均频率无系统相关性。结论：r（RR）是一种新的评价动态心跳间期行为和交感迷走神经平衡的工具。这种非线性的方法可能会提供新的见解植物神经紊乱。
We have recently demonstrated that the overnight profiles of cardiac interbeat autocorrelation coefficient of R-R intervals (r(RR)) calculated at 1-min intervals are related to the changes in sleep electroencephalographic (EEG) mean frequency, which reflect depth of sleep. Other quantitative measures of the Poincare plots, i.e., the standard deviation of normal R-R intervals (SDNN) and the root mean square difference among successive R-R normal intervals (RMSSD), are commonly used to evaluate heart rate variability. The present study was designed to compare the nocturnal profiles of r(RR), SDNN, and RMSSD with the R-R spectral power components: high-frequency (HF) power, reflecting parasympathetic activity; low-frequency (LF) power, reflecting a predominance of sympathetic activity with a parasympathetic component; and the LF-to-HF ratio (LF/HF), regarded as an index of sympathovagal balance. r(RR), SDNN, RMSSD, and the spectral power components were calculated every 5 min during sleep in 15 healthy subjects. The overnight profiles of r(RR) and LF/HF showed coordinate variations with highly significant correlation coefficients (P < 0.001 in all subjects). SDNN correlated with LF power (P < 0.001), and RMSSD correlated with HF power (P < 0.001). The overnight profiles of r(RR) and EEG mean frequency were found to be closely related with highly cross-correlated coefficients (P < 0.001). SDNN and EEG mean frequency were also highly cross correlated (P < 0.001 in all subjects but 1). No systematic relationship was found between RMSSD and EEG mean frequency. In conclusion, r(RR) appears to be a new tool for evaluating the dynamic beat-to-beat interval behavior and the sympathovagal balance continuously during sleep. This nonlinear method may provide new insight into autonomic disorders.