Dementia detection from brain activity during sleep
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".