Diagnosing Alzheimer’s Disease and Frontotemporal Dementia Using Machine Learning and EEG
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".