Detection of Dementia: Using Electroencephalography and Machine Learning
Bibliographic record
Abstract
Introduction This article serves as a background to an emerging field and aims to investigate the use of Electroencephalography signals in detecting dementia. It offers a promising approach for individuals with dementia, as electroencephalography provides a non-invasive measure of brain activity during language tasks. Method: The methodological core of this study involves implementing various electroencephalography feature extraction and selection techniques, along with the use of machine learning algorithms for analyzing the signals to identify patterns indicative of dementia. In terms of results, our analysis showed that most individuals likely to have dementia are in the 60-69 age bracket, with a higher incidence in females. Result: Notably, the K-means algorithm achieved the highest Silhouette Score at approximately 0.295. Additionally, Decision Tree and Random Forest models achieved the best accuracy at 95.83%, slightly outperforming the support vector machines and Logistic Regression models, which also showed good accuracy at 91.67%. Conclusion: The conclusion drawn from this article is that electroencephalography signals, analyzed with machine learning algorithms, can be effectively used to detect dementia, with Decision Tree and Random Forest models showing promise for future non-invasive diagnostic tools.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 0.001 |
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".