Diagnosing Alzheimer’s Disease and Frontotemporal Dementia Using Machine Learning and EEG
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are types of neurodegenerative dementias characterized by progressive cognitive decline. Electroencephalography (EEG) signal analysis is becoming a promising, inexpensive method to early diagnose AD and FTD. Prior research has applied machine learning to classify AD and healthy patients (HC) based on EEG readings, but not distinguishing between AD, FTD, and healthy patients. In the present paper, power spectral features will be extracted from raw EEG recordings for random forest classifiers and artificial neural networks to differentiate among AD, FTD, and HC patients. The first two minutes of 88 EEG recordings were used from the 2nd Department of Neurology in Thessaloniki. The models achieved 96%, 92%, and 95% accuracy when dealing with binary classification problems (AD and HC, AD and FTD, HC and FTD) and 90.4% for all three classes. The accuracies improve upon the results of previous literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it