EEG biomarkers for Alzheimer's disease: A novel automated pipeline for detecting and monitoring disease progression
Bibliographic record
Abstract
BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder that profoundly alters brain function and organization. Currently, there is a lack of validated functional biomarkers to aid in diagnosing and classifying AD. Therefore, there is a pressing need for early, accurate, non-invasive, and accessible methods to detect and characterize disease progression. Electroencephalography (EEG) has emerged as a minimally invasive technique to quantify functional changes in neural activity associated with AD. However, challenges such as poor signal-to-noise ratio-particularly for resting-state (rsEEG) recordings-and issues with standardization have hindered its broader application.ObjectiveTo conduct a pilot analysis of our custom automated preprocessing and feature extraction pipeline to identify indicators of AD and correlates of disease progression.MethodsWe analyzed data from 36 individuals with AD and 29 healthy participants recorded using a standard 19-channel EEG and features were processed using our custom end-t-end pipeline. Various features encompassing amplitude, power, connectivity, complexity, and microstates were extracted. Unsupervised machine learning (uniform manifold approximation and projection) and supervised learning (random forest classifiers with nested cross-validation) were used to characterize the dataset and identify differences between AD and healthy groups.ResultsOur pipeline successfully detected several new and previously established EEG-based measures indicative of AD status and progression, demonstrating strong external validity.ConclusionsOur 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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.003 |
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".