Dementia detection from brain activity during sleep - data and code

**Study Objectives.** Dementia is a growing cause of disability and loss of
independence in the elderly, yet remains largely underdiagnosed. Early
detection and classification of dementia can help close this diagnostic gap
and improve management of disease progression. Altered oscillations in brain
activity during sleep are an early feature of neurodegenerative diseases and
can be used to identify those on the verge of cognitive decline.

**Methods.** Our observational cross-sectional study used a clinical dataset
of 10,784 polysomnograms from 8,044 participants. Sleep macro- and micro-
structural features were extracted from the electroencephalogram (EEG).
Microstructural features were engineered from spectral band powers, EEG
coherence, spindles, and slow oscillations. Participants were classified as
dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN)
based on clinical diagnosis, Montreal Cognitive Assessment, Mini-Mental State
Exam scores, clinical dementia rating, and prescribed medications. We trained
logistic regression, support vector machine, and random forest models to
classify patients into DEM, MCI, and CN groups.

**Results.** For discriminating DEM versus CN, the best model achieved an area
under receiver operating characteristic curve (AUROC) of 0.78 and area under
precision-recall curve (AUPRC) of 0.22. For discriminating MCI versus CN, the
best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM
or MCI versus CN, the best model achieved an AUROC of 0.76 and AUPRC of 0.32.

**Conclusions.** Our dementia classification algorithms show promise for
incorporating dementia screening techniques using routine sleep EEG. The
findings strengthen the concept of sleep as a window into neurodegenerative
diseases.