Department of Physiology, Faculty of Biology, University of Seville, Seville, Spain.
Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
J Sleep Res. 2021 Aug;30(4):e13233. doi: 10.1111/jsr.13233. Epub 2020 Nov 16.
The electroencephalographic signal constitutes the main sign classically used for the identification of states of alertness. However, activities in the high frequency (>100 Hz) range have not been properly studied despite their high potential for sleep scoring in rodents. In the present study, we designed a method for the identification of the sleep-wake states in rats by exclusively using high-frequency activities of the electroencephalogram. By calculating the ratio between the amplitude of the electroencephalographic signal from 110 to 200 Hz and from 110 to 300 Hz, we obtained an index that had values that were low during wakefulness, intermediate during non-REM sleep and high during REM sleep. This high-frequency index (HiFI) allowed the identification of each state without the need to study other signs such as muscle activity or eye movements. To evaluate the performance of the index, we compared it with the conventional scoring of the sleep-wake cycle based upon the study of the electromyogram and delta (0.5-4 Hz), theta (6-9 Hz) and sigma (10-14 Hz) bands of the electroencephalogram. The index had an accuracy of 90.43 ± 1.91% (Cohen's kappa value of 0.82), confirming that the study of the high-frequency activities of the electroencephalogram was sufficient to reliably identify alertness states in the rat. Compared to other sleep-scoring methods, the HiFI has several advantages. It only requires one electroencephalography electrode, thus reducing the severity of the surgical preparation of the experimental animal, and its calculation is very simple, so it can be easily implemented online to classify sleep-wake states in real time.
脑电图信号构成了经典用于识别警觉状态的主要标志类别。然而，尽管高频（> 100 Hz）活动在啮齿动物的睡眠评分中具有很高的潜力，但它们仍未得到适当的研究。在本研究中，我们设计了一种仅使用脑电图的高频活动来识别大鼠睡眠-觉醒状态的方法。通过计算脑电图信号在 110 到 200 Hz 和 110 到 300 Hz 之间的幅度比，我们获得了一个在觉醒期间值较低、在非快速眼动睡眠期间值中等、在快速眼动睡眠期间值较高的指数。这个高频指数（HiFI）允许在无需研究肌肉活动或眼球运动等其他标志的情况下识别每种状态。为了评估该指数的性能，我们将其与基于肌电图和 delta（0.5-4 Hz）、theta（6-9 Hz）和 sigma（10-14 Hz）频段的脑电图研究的传统睡眠-觉醒周期评分进行了比较。该指数的准确率为 90.43 ± 1.91%（Cohen's kappa 值为 0.82），证实了研究脑电图的高频活动足以可靠地识别大鼠的警觉状态。与其他睡眠评分方法相比，HiFI 具有几个优势。它只需要一个脑电图电极，从而减轻了实验动物手术准备的严重程度，并且其计算非常简单，因此可以很容易地在线实现，以实时分类睡眠-觉醒状态。
