Retracted: A Non-Linear Support Vector Machine Approach to Testing for Migraine with Aura Using Electroencephalography
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
In this paper a new migraine analysis method is proposed using EEG (electroencephalography) signals to characterize migraine patients with aura (MwA). The objective of this work is to implement a technique for characterizing and extracting significant, robust and informative features from EEG signals which are representative of the interictal migraine brain state. We extract three brain characteristics using brain network analysis of alpha phase synchronization; transient abnormality analysis using wavelet scale; and finally joint time-frequency analysis using AR modeling. Feature selection and reduction techniques were performed on the sub-features of these three mutually independent features, to combat the over-fit problem as well as to maximize the generality of the classifier. Interpretation of the reduced features resembled to previous migraine studies. Furthermore, extracted features were used as inputs to a 10-fold cross validated non-linear support vector machine (SVM) classifier. The results showed a 92.9% classification accuracy for MwA in the interictal stage from the normal control (NC) group. Findings suggest electrical features for the predisposition of migraine which can lead to possible preventative interventions in the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".