Research Center for Adaptive Data Analysis and Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taoyuan, Taiwan.
J Neurosci Methods. 2013 Oct 15;219(2):233-9. doi: 10.1016/j.jneumeth.2013.08.008. Epub 2013 Aug 18.
The multi-mode modulation is a key feature of sleep EEG. And the short-term fractal property reflects the sympathovagal modulation of heart rate variability (HRV). The properties of EEG and HRV strongly correlated with sleep status and are interesting in clinic diagnosis.
19 healthy female subjects were included for over-night standard polysomnographic study. Hilbert Huang transform (HHT) was used to characterize the temporal features of slow- and fast-wave oscillations decomposed from sleep EEG at different stages. Masking signals were used for solving the mode-mixing problem in HHT. On the other hand, detrended fluctuation analysis (DFA) was used to assess short-term property of HRV denoted as DFA α1, which reflects the temporal activity of autonomic nerve system (ANS). Thus, the dynamic interaction between sleep EEG and HRV can be examined through the relationship between the features of sleep EEG and DFA α1 of HRV.
The frequency feature of sleep EEG serves as a good indicator for the depth of sleep during non-rapid eye movement (NREM) sleep, and amplitude feature of fast-wave oscillation is a good index for distinguishing rapid eye movement (REM) from NREM sleep.
The relationship between DFA α1 of HRV and the mean amplitude of fast-wave oscillation of sleep EEG affirmed with Pearson correlation coefficient is more significant than the correlation verified by the traditional spectral analysis.
The dynamic properties of sleep EEG and HRV derived by EMD and DFA represent important features for cortex and ANS activities during sleep.
多模态调制是睡眠脑电图的一个关键特征。而短期分形性质反映了心率变异性（HRV）的交感神经和副交感神经调制。脑电图和 HRV 的特性与睡眠状态密切相关，在临床诊断中很有趣。
19 名健康女性受试者被纳入过夜标准多导睡眠图研究。希尔伯特黄变换（HHT）用于描述从不同阶段的睡眠 EEG 中分解出的慢波和快波振荡的时间特征。掩蔽信号用于解决 HHT 中的模式混合问题。另一方面，去趋势波动分析（DFA）用于评估 HRV 的短期特性，记为 DFAα1，它反映了自主神经系统（ANS）的时间活动。因此，通过睡眠 EEG 特征和 HRV 的 DFAα1 之间的关系，可以检查睡眠 EEG 和 HRV 之间的动态相互作用。
睡眠 EEG 的频率特征是评估非快速眼动（NREM）睡眠深度的良好指标，快波振荡的幅度特征是区分快速眼动（REM）和 NREM 睡眠的良好指标。
通过 Pearson 相关系数证实的 HRV 的 DFAα1 与睡眠 EEG 的快波振荡平均幅度之间的关系比通过传统频谱分析验证的相关性更为显著。
由 EMD 和 DFA 得出的睡眠 EEG 和 HRV 的动态特性代表了睡眠期间皮质和 ANS 活动的重要特征。
