Lu Shan, Yang Hao, Xiao Dongping, Huang Ying
Institute of Electrical Engineering Theory, Chongqing University, Chongqing 400030, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2006 Oct;23(5):964-7.
The analysis of heart rate variability (HRV) has become a tool for noninvasively detecting the cardiovascular modulation of autonomic nervous system. Traditional analysis in frequency domain mainly includes calculating the power and detecting the peak frequency of each physiological frequency band. Whether employing the classical method or AR model to estimate the spectrum, the approximate stationarity of HRV is presupposed. Only in short term analysis can data meet this condition, while in long term the nonstationarity of HRV notably appears. A dynamic analysis method based on wavelet transform was proposed in this paper, which not only can obtain the traditional indices in frequency domain, but can compute their dynamic values varying with time, called short-time power and short-time LF/HF ratio. The latter can dynamically evaluate the activity of autonomic nervous. Finally the method was applied to trace the balance of autonomic nervous in Atropin drag experiment.
心率变异性(HRV)分析已成为一种用于无创检测自主神经系统心血管调节的工具。频域中的传统分析主要包括计算功率和检测每个生理频段的峰值频率。无论是采用经典方法还是自回归(AR)模型来估计频谱,都预先假定了HRV的近似平稳性。只有在短期分析中数据才能满足这一条件,而在长期分析中HRV的非平稳性则会显著显现。本文提出了一种基于小波变换的动态分析方法,该方法不仅可以获得频域中的传统指标,还可以计算它们随时间变化的动态值,即短时功率和短时低频/高频比值。后者可以动态评估自主神经的活动。最后将该方法应用于阿托品药物实验中自主神经平衡的追踪。