Entropy and Fractal Analysis of EEG Signals for Early Detection of Alzheimer's Dementia
Bibliographic record
Abstract
The rapid progression of diseases in the elderly, such as Alzheimer's Dementia (AD), necessitates effective early detection mechanisms to ensure appropriate healthcare provision.Given the consistently increasing prevalence of AD, the potential for emerging socio-economic challenges is significant.This underlines the importance of developing early detection strategies to mitigate the progression of this disease.Electroencephalograms (EEG) present a promising avenue for the early diagnosis of AD.EEG signals harbor crucial information pertaining to neuronal death triggered by amyloid plaque accumulation, a characteristic feature of AD.Spectral analysis reveals a deceleration in signal activity in AD patients when compared to healthy elderly individuals.However, this method is frequently compromised by low-frequency noise, necessitating the exploration of alternative approaches for analyzing EEG signal features for early AD detection.Considering the complex nature of EEG signals, it is hypothesized that pathological conditions, such as AD, may induce alterations in signal complexity.In this study, an early detection model for AD was simulated utilizing an approach that focused on EEG signal complexity.Complexity analysis, incorporating Spectral Entropy (SpecEn) and fractal dimensions, was calculated across 19 EEG channels from a total of 34 subjects (16 normal and 18 with Mild Cognitive Impairment (MCI)).Performance validation of the proposed method was achieved through Linear Discriminant Analysis (LDA), yielding an accuracy of 82.4%, specificity of 77.8%, and sensitivity of 87.5%.The findings from this study suggest that EEG analysis can serve as a reliable tool for the early detection of AD.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 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".