Complex wavelet algorithm for computer‐aided diagnosis of Alzheimer's disease
Bibliographic record
Abstract
Electroencephalography signals are used for computer‐aided diagnosis of Alzheimer's disease. Therefore, extracting critical features that belong to Alzheimer's signals are useful and tedious for neural network classification due to the high‐frequency non‐stationary components. For this purpose, time–frequency analysis and the multi‐resolution capability of wavelets represent an attractive choice. However, fluctuations of the transformed coefficients and the absence of phase information make the process less accurate in certain scenarios. Because of this, complex wavelet transform has been selected to handle Alzheimer's signals. Moreover, the importance of calculating an optimal threshold value has been highlighted, usually by means of Shannon entropy as a helpful threshold identifier of the complex wavelet transform used to produce significant results. The effectiveness of Tsallis entropy instead of Shannon entropy in handling Alzheimer's signals is evaluated, the former giving place to better features for neural network classification. As a result, accuracy has been improved from 90 to 95% using Tsallis entropy. Hence, this new proposal boosts the opportunity to reduce mortality rates by detecting the disease accurately.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".