GR.4 EEG biomarkers for Alzheimer’s Disease: a novel automated pipeline for detecting and monitoring disease progression
Bibliographic record
Abstract
Background: Electroencephalography (EEG) has emerged as a minimally invasive technique to quantify functional changes in neural activity associated with neurodegenerative disorders such as Alzheimer’s Disease (AD). Given its non-invasive approach, EEG has the potential to fill the pressing gap forearly, accurate, and accessible methods to detect and characterize disease progression in AD. Methods: To address these challenges, we conducted a pilot analysis of a custom machine learning-based automated preprocessing and feature extraction pipeline to identify indicators of AD and correlates of disease progression. Results: Our pipeline successfully detected several new and previously established EEG-based measures indicative of AD status and progression. Key findings included alterations in delta and theta band power, network connectivity disruptions, and increased slowing of brain rhythms. Additionally, we observed strong correlations between EEG-derived metrics and clinical measures such as Mini-Mental State Examination (MMSE) scores, supporting the external validity of our approach. These findings highlight the sensitivity of EEG biomarkers in differentiating between early and late stages of AD. Conclusions: Our findings suggest that this automated approach provides a promising initial framework for implementing EEG biomarkers in the AD patient population, paving the way for improved diagnostic and monitoring strategies.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".