Archetypal Analysis for the Characterization of Distinct Patterns in Mouse Sleep Data

Sleep in mice is a heterogeneous state where the currently used three main sleep stages may not adequately capture underlying sleep-wake dynamics, which are believed to be a continuum as opposed to be defined by discrete states. We presently explore how archetypal analysis (AA) can be used to extract distinct characteristics in sleep data and to represent the sleep as a continuum. For the analyses, we consider relative EEG power of six frequency bands and EMG RMS in 4 s epochs as obtained from 36 unique mice (18 wild-type (WT) and 18 narcolepsy (NT) from a diphtheria toxin A (DTA) mouse model). Specifically, we investigate how the AA extracted sample-specific profiles defined by a continuum of the archetypes reflect sleep stages, mice type (WT/NT) as well as inherent mouse variability (mice ID) as quantified by their normalized mutual information (NMI). We find that the AA robustly extracts distinct characteristics reflecting specific aspects of frequency band and RMS activity of the sleep data. We further observe that the extracted sample specific profiles defining a continuum between these distinct characteristics share correspondence to sleep stages, mouse type and mouse ID with the most pronounced correspondence being to sleep stages. Whereas these correspondences are significant when compared using permutation testing by randomly shuffling the sample specific labels, the correspondence is relatively low (NMI≤0.2), pointing to the distinct aspects identified in the data reflecting richer properties of the sleep data. AA is a promising framework for the analysis of sleep data providing easily interpretable characterizations of the distinct aspects in the data notably also representing samples as a continuum of these identified aspects. The approach readily generalizes to higher-dimensional sleep data providing an interesting versatile tool to further our understand of the properties and structure of sleep.Clinical relevance Archetypal analysis provides a novel and interpretable characterization of sleep data in terms of their distinct aspects. Additionally, each observation can be defined as a continuum enabling further understanding of sleep characteristics in health and disease.