In Sleep
STUDY OBJECTIVES : Dementia is a growing cause of disability and loss of independence in the elderly, yet remains largely under-diagnosed. 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 is an early feature of neurodegenerative diseases and be used to identify those on the verge of cognitive decline.
METHODS : Our observational cross-sectional study used a clinical dataset of 10,784 polysomnography from 8,044 participants. Sleep macro-and micro-structural features were extracted from the electroencephalogram (EEG). Micro-structural features were engineered from spectral band powers, EEG coherence, spindle, and slow oscillations. Participants were classified as dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN) based on clinical diagnosis, Montreal Cognitive Assessment (MoCA), Mini-Mental State Exam (MMSE) scores, Clinical Dementia Rating (CDR), 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 vs. 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 vs. CN, the best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM or MCI vs. 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.
Ye Elissa M, Sun Haoqi, Krishnamurthy Parimala V, Adra Noor, Ganglberger Wolfgang, Thomas Robert J, Lam Alice D, Westover M Brandon
2022-Nov-30
Biomarker, Dementia, EEG, Machine Learning, Sleep
