School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia.
Philosophy Department, Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia; Monash Centre for Consciousness and Contemplative Studies, Monash University, Melbourne, Victoria, Australia.
Sleep Med. 2022 Oct;98:39-52. doi: 10.1016/j.sleep.2022.06.013. Epub 2022 Jun 21.
The widely used guidelines for sleep staging were developed for the visual inspection of electrophysiological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time-series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states.
广泛使用的睡眠分期指南是为了通过人眼对电生理记录进行视觉检查而制定的。因此，这些规则反映了这些数据中有限的特征范围，因此在准确捕捉与睡眠相关的生理变化方面受到限制。在这里，我们提出了一个新的分析框架，该框架使用来自 hctsa 软件的 7700 多个时间序列特征来广泛描述睡眠动态。我们使用聚类根据时间序列特征的相似性对睡眠阶段进行分类，而不依赖于既定的评分惯例。由此产生的睡眠结构与视觉评分定义的睡眠结构有很大的重叠。然而，我们也观察到我们的方法与传统评分之间存在差异。这种分歧主要源于 hctsa 特征的广泛特征描述，它捕捉到了在传统定义的睡眠阶段内被视觉评分忽略的独特时间序列特性。最后，我们报告了高度区分阶段的时间序列特征。我们的框架为睡眠亚阶段的基于数据的探索奠定了基础，并具有识别睡眠障碍和有意识睡眠状态新特征的巨大潜力。
