Biomedical Engineering Group, University of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain.
Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Valladolid, Spain.
J Neural Eng. 2021 Mar 16;18(3). doi: 10.1088/1741-2552/abe8ad.
. Nested into slow oscillations (SOs) and modulated by their up-states, spindles are electrophysiological hallmarks of N2 sleep stage that present a complex hierarchical architecture. However, most studies have only described spindles in basic statistical terms, which were limited to the spindle itself without analyzing the characteristics of the pre-spindle moments in which the SOs are originated. The aim of this study was twofold: (a) to apply spectral and temporal measures to the pre-spindle and spindle periods, as well as analyze the correlation between them, and (b) to evaluate the potential of these spectral and temporal measures in future automatic detection algorithms.. An automatic spindle detection algorithm was applied to the overnight electroencephalographic recordings of 26 subjects. Ten complementary features (five spectral and five temporal parameters) were computed in the pre-spindle and spindle periods after their segmentation. These features were computed independently in each period and in a time-resolved way (sliding window). After the statistical comparison of both periods, a correlation analysis was used to assess their interrelationships. Finally, a receiver operating-characteristic (ROC) analysis along with a bootstrap procedure was conducted to further evaluate the degree of separability between the pre-spindle and spindle periods.. The results show important time-varying changes in spectral and temporal parameters. The features calculated in pre-spindle and spindle periods are strongly and significantly correlated, demonstrating the association between the pre-spindle characteristics and the subsequent spindle. The ROC analysis exposes that the typical feature used in automatic spindle detectors, i.e. the power in the sigma band, is outperformed by other features, such as the spectral entropy in this frequency range.. The novel features applied here demonstrate their utility as predictors of spindles that could be incorporated into novel algorithms of automatic spindle detectors, in which the analysis of the pre-spindle period becomes relevant for improving their performance. From the clinical point of view, these features may serve as novel precision therapeutic targets to enhance spindle production with the aim of improving memory, cognition, and sleep quality in healthy and clinical populations. The results evidence the need for characterizing spindles in terms beyond power and the spindle period itself to more dynamic measures and the pre-spindle period. Physiologically, these findings suggest that spindles are more than simple oscillations, but nonstable oscillatory bursts embedded in the complex pre-spindle dynamics.
. 纺锤波嵌套于慢波（SOs）中，并受其活动状态调制，是 N2 睡眠阶段的电生理标志，呈现出复杂的层次结构。然而，大多数研究仅以基本的统计术语描述纺锤波，这些描述仅限于纺锤波本身，而没有分析产生 SO 的前纺锤波时刻的特征。本研究的目的有两个：（a）应用谱和时的测量方法分析前纺锤波和纺锤波的特征，并分析它们之间的相关性；（b）评估这些谱和时的测量方法在未来自动检测算法中的潜力。. 我们将一种自动纺锤波检测算法应用于 26 名受试者的整夜脑电图记录。在前纺锤波和纺锤波时期，我们在分段后计算了 10 个补充特征（5 个谱参数和 5 个时参数）。这些特征分别在每个时期和时间分辨（滑动窗口）的方式进行计算。在对两个时期进行统计比较后，我们使用相关性分析来评估它们之间的关系。最后，我们进行了接收者操作特征（ROC）分析和引导程序，以进一步评估前纺锤波和纺锤波时期之间的可分离程度。. 结果显示，在谱和时参数方面存在重要的时变变化。前纺锤波和纺锤波时期计算出的特征具有很强的相关性和显著的相关性，证明了前纺锤波特征与随后的纺锤波之间的关联。ROC 分析表明，自动纺锤波检测中常用的特征，即 sigma 频段的功率，不如其他特征，如该频段的谱熵，具有更好的可分离性。这里应用的新特征证明了它们作为纺锤波预测因子的有效性，可将其纳入自动纺锤波检测的新算法中，其中对前纺锤波时期的分析对于提高它们的性能变得很重要。从临床的角度来看，这些特征可能成为新的精准治疗靶点，以增加纺锤波的产生，从而提高健康和临床人群的记忆、认知和睡眠质量。结果表明，需要超越功率和纺锤波本身来描述更具动态性的特征和前纺锤波时期。从生理学的角度来看，这些发现表明纺锤波不仅仅是简单的振荡，而是嵌入在复杂的前纺锤波动力学中的不稳定振荡爆发。
