Dementia detection from brain activity during sleep
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".