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Dementia detection from brain activity during sleep
Author(s) -
Ye Elissa M,
Sun Haoqi,
Krishnamurthy Parimala V,
Lam Alice D,
Westover M Brandon
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.058718
Subject(s) - dementia , clinical dementia rating , electroencephalography , audiology , logistic regression , montreal cognitive assessment , binary classification , psychology , cognition , psychiatry , medicine , disease , artificial intelligence , computer science , support vector machine
Abstract Background 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 may help close this diagnostic gap and improve management of disease progression. EEG sleep patterns have been identified as a potential biomarker to detect Alzheimer’s disease and other neurodegenerative diseases. Method From a dataset of 9834 polysomnograms, sleep architecture and microstructure features such as frequency band powers, EEG coherence, and spindle density were extracted. Patients were labeled as belonging to dementia, mild cognitive impairment (MCI), or cognitively normal (CN) groups based on clinical diagnosis, Montreal Cognitive Assessment (MoCA), Mini‐Mental State Exam (MMSE) scores, Clinical Dementia Rating (CDR) and medications. We trained logistic regression, random forest, and XGBoost models to classify patients into Dementia, MCI, and CN groups. Result Nested cross validation results show an AUC of 0.81 (F1 = 0.76) for binary classification of dementia vs CN groups and a mean AUC of 0.75 (F1 =0.57) for multiclass classification of dementia vs. MCI vs CN groups. REM latency, spindle activity, duration, frequency, slow wave oscillation, delta/alpha band power in wake, N1 theta/alpha band power in N1 were among the top weighted features. Conclusion Our dementia classification algorithms show promise for incorporating dementia screening techniques into routine sleep EEG and providing diagnostic, monitoring, and prognostication capabilities.