Quantitative EEG and Statistical Mapping of Wakefulness and REM Sleep in the Evaluation of Mild to Moderate Alzheimer’s Disease
Bibliographic record
Abstract
Statistical probability mapping was used to quantify and localize EEG differences between 27 patients with Alzheimer's disease (AD) and 25 age- and gener-matched controls. Differences in mean activity in four EEG frequency bands (delta, theta, alpha, beta) for wakefulness and for REM sleep were examined, t-statistic maps clearly highlighted common pattern anomalies in AD patients in the two states. More specifically, Alzheimer patients were more affected than control subjects in parieto-temporal and frontal regions. These differences were more prominent in REM sleep and consisted primarily in an increase in absolute delta and theta activities, and a decrease in absolute alpha and beta activities. Discriminant analysis, using a ratio of slow over fast frequencies, yielded a classification rate of 90.4% (sensitivity 81.5%, specificity 100%) for REM sleep. For wakefulness, the same measure allowed correct classification of 80.8% of the subjects (sensitivity 66.7%, specificity 96%).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".