Dementia detection from brain activity during sleep

• Published in: Sleep (New York, N.Y.) Vol. 46; no. 3; p. 1
• Main Authors: Ye, Elissa M, Sun, Haoqi, Krishnamurthy, Parimala V, Adra, Noor, Ganglberger, Wolfgang, Thomas, Robert J, Lam, Alice D, Westover, M Brandon
• Format: Journal Article
• Language: English
• Published: US Oxford University Press 09-03-2023
• Subjects: Aged; Alzheimer Disease; Brain; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - psychology; Cross-Sectional Studies; Dementia; Dementia - diagnosis; Development and progression; Electroencephalography; Humans; Machine learning; Medical research; Medicine, Experimental; Nervous system diseases; Sleep; Sleep, Health, and Disease; sleep; dementia; machine learning; biomarker; EEG
• ISSN: 0161-8105; 1550-9109
• DOI: 10.1093/sleep/zsac286

Abstract
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 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 8044 participants. Sleep macro- and micro-structural features were extracted from the electroencephalogram (EEG). Microstructural 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, 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.
Graphical Abstract
Graphical Abstract