Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cerebrospinal fluid (CSF) sampling or costly neuroimaging, limiting scalability. Sleep quantitative electroencephalography (qEEG) provides a non-invasive measure of brain function and may capture early AD-related neural alterations; however, the high dimensionality and complexity of these features limit interpretation with conventional approaches, requiring multivariate methods. The objective of this study is to assess whether sleep qEEG features are associated with biologically meaningful stratification of AD in accordance with the NIA-AA 2024 framework. Forty-two patients with mild-to-moderate AD underwent overnight polysomnography and CSF biomarker assessment, while 58 cognitively unimpaired controls provided sleep EEG recording. EEG signals from four channels were preprocessed, segmented by sleep stage, and characterized using linear, spectral, and non-linear features. Dimensionality reduction was performed using principal component analysis (PCA), guided by random forest-based relevance to CSF biomarkers (A