Zandi Ali Shahidi, Boudreau Philippe, Boivin Diane B, Dumont Guy A
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:6297-300. doi: 10.1109/EMBC.2013.6610993.
We propose a novel entropy-based measure to quantify the circadian variations of scalp electroencephalogram (EEG) by analyzing waking epochs of nap opportunities under an ultradian sleep-wake cycle (USW) protocol. To compute this circadian measure for a nap opportunity, each waking epoch (~1 sec) is decomposed using wavelet packet transform and the relative energy for the desired frequency band (here, 10-12 Hz) is calculated. Then, in a bootstrapping procedure, a shape statistic (skewness or kurtosis) of the relative energy distribution, after each resampling, is computed. Finally, the probability density function of this statistic is estimated, and the corresponding differential entropy is considered as the circadian measure. This measure was evaluated using EEG recordings from 4 healthy subjects during a 72-h USW procedure. According to the results, the proposed measure showed a significant circadian variation both for individual and group data, with peak values occurring near the core body temperature minimum. The performance of the entropy-based measure was also compared with that of two other measures, namely mean energy logarithm and mean energy ratio, revealing the superiority of this measure.
我们提出了一种基于熵的新方法，通过分析超日睡眠-觉醒周期（USW）协议下小睡机会的清醒时段，来量化头皮脑电图（EEG）的昼夜节律变化。为了计算小睡机会的这种昼夜节律指标，使用小波包变换对每个清醒时段（约1秒）进行分解，并计算所需频段（此处为10 - 12Hz）的相对能量。然后，在自举过程中，每次重采样后计算相对能量分布的形状统计量（偏度或峰度）。最后，估计该统计量的概率密度函数，并将相应的微分熵视为昼夜节律指标。在一项72小时的USW过程中，使用4名健康受试者的EEG记录对该指标进行了评估。结果显示，所提出的指标在个体和群体数据中均表现出显著的昼夜节律变化，峰值出现在核心体温最低值附近。还将基于熵的指标的性能与其他两个指标（即平均能量对数和平均能量比）进行了比较，结果表明该指标具有优越性。
