Department of Mathematics and Information Sciences, Northumbria University, , Newcastle Upon Tyne NE1 8ST, UK.
J R Soc Interface. 2014 Feb 5;11(93):20131112. doi: 10.1098/rsif.2013.1112. Print 2014 Apr 6.
For the first time, fractal analysis techniques are implemented to study the correlations present in sleep actigraphy for individuals suffering from acute insomnia with comparisons made against healthy subjects. Analysis was carried out for 21 healthy individuals with no diagnosed sleep disorders and 26 subjects diagnosed with acute insomnia during night-time hours. Detrended fluctuation analysis was applied in order to look for 1/f-fluctuations indicative of high complexity. The aim is to investigate whether complexity analysis can differentiate between people who sleep normally and people who suffer from acute insomnia. We hypothesize that the complexity will be higher in subjects who suffer from acute insomnia owing to increased night-time arousals. This hypothesis, although contrary to much of the literature surrounding complexity in physiology, was found to be correct-for our study. The complexity results for nearly all of the subjects fell within a 1/f-range, indicating the presence of underlying control mechanisms. The subjects with acute insomnia displayed significantly higher correlations, confirmed by significance testing-possibly a result of too much activity in the underlying regulatory systems. Moreover, we found a linear relationship between complexity and variability, both of which increased with the onset of insomnia. Complexity analysis is very promising and could prove to be a useful non-invasive identifier for people who suffer from sleep disorders such as insomnia.
首次运用分形分析技术研究急性失眠患者睡眠活动图中的相关性，并与健康个体进行比较。分析了 21 名无睡眠障碍的健康个体和 26 名夜间确诊为急性失眠的个体。为寻找指示高复杂性的 1/f 波动，应用去趋势波动分析。目的是研究复杂性分析是否可以区分正常睡眠者和急性失眠者。我们假设由于夜间觉醒增加，急性失眠患者的复杂性会更高。尽管该假设与生理复杂性的许多文献相悖，但我们的研究结果表明，该假设是正确的。几乎所有受试者的复杂性结果都落在 1/f 范围内，表明存在潜在的控制机制。急性失眠患者的相关性显著更高，通过显著性检验得到证实，这可能是潜在调节系统活动过多的结果。此外，我们发现复杂性和可变性之间存在线性关系，随着失眠的发生，两者都增加。复杂性分析非常有前景，可能成为失眠等睡眠障碍患者的一种有用的非侵入性识别方法。
