Rassi D, Mishin A, Zhuravlev Y E, Matthes J
School of Health Science, University of Wales-Swansea, Singleton Park, Swansea SA2 8PP, Wales, UK.
Early Hum Dev. 2005 Apr;81(4):341-50. doi: 10.1016/j.earlhumdev.2004.09.002. Epub 2004 Oct 22.
A fuller understanding of the neural control mechanisms of heart rate during the early stages of human development would be of great value to obstetric and neonatal management. In this paper, we investigate the correlation between heart rate variability (HRV) and other physiological parameters such as blood pressure and respiration in preterm neonates with the aim of developing a numerical model to explain and predict heart rate variability.
All the required data are readily available for premature babies who are routinely monitored while being nursed in intensive care, and we have collected large data sets for a random group of such neonates. For the quantitative analysis of the data, we have developed a time domain correlation method, which has a number of advantages over the more commonly used power spectral analysis. We have been able to study the dynamics of the different frequency components of HRV by this method.
Highly correlated behaviour of the different HRV components, previously observed in our work on fetal HRV, is also present in the neonate, with similar characteristic time constants. Furthermore, the correlation of high-frequency (HF) oscillations of HRV with respiration and that of low-frequency (LF) oscillations of HRV with blood pressure are demonstrated on timescales of a single oscillation. In neonates receiving artificial ventilation, the correlation between HRV and respiration depends on the type of ventilation involved and assumes opposite polarities for the two main types of equipment currently in use.
We demonstrate that it is possible to analyse HRV quantitatively by calculating the relative gains and characteristic time constants for the correlated parameters and components.
更全面地了解人类发育早期心率的神经控制机制对产科和新生儿管理具有重要价值。在本文中,我们研究了早产新生儿心率变异性(HRV)与其他生理参数(如血压和呼吸)之间的相关性,旨在建立一个数值模型来解释和预测心率变异性。
对于在重症监护室接受常规监测的早产儿,所有所需数据都很容易获取,我们已经为一组随机选取的此类新生儿收集了大量数据集。为了对数据进行定量分析,我们开发了一种时域相关方法,该方法相对于更常用的功率谱分析具有许多优势。通过这种方法,我们能够研究HRV不同频率成分的动态变化。
在我们之前关于胎儿HRV的研究中观察到的不同HRV成分的高度相关行为,在新生儿中也存在,且具有相似的特征时间常数。此外,在单个振荡的时间尺度上,证明了HRV的高频(HF)振荡与呼吸之间以及HRV的低频(LF)振荡与血压之间的相关性。在接受人工通气的新生儿中,HRV与呼吸之间的相关性取决于所涉及的通气类型,并且对于目前使用的两种主要设备类型呈现相反的极性。
我们证明,通过计算相关参数和成分的相对增益和特征时间常数,可以对HRV进行定量分析。