Analysis of electrooculogram (EOG) signals in studying myasthenia gravis
Bibliographic record
Abstract
Myasthenia Gravis (MG) is a neuromuscular disorder that induces muscle weakness and fatigue which can be fatal. A common precursor for severe form of MG is ocular MG. In this thesis, we explored signal processing methodologies for early stage detection of MG using electrooculogram (EOG) signals. An EOG signal database consisting of 62 control and 16 MG (mild to moderate) subjects were analyzed for eye movement characteristics and EOG signal morphologies using time domain and wavelet domain techniques. A linear discriminant analysis (LDA) based classifier was used to quantify the ability of features in separating MG from control samples. Average overall classification accuracy achieved by the proposed method for the best time domain feature (average rise rate) and best wavelet feature (scale band energy) was 82.5% (P<0.01, AUC=0.887) and 83.8% (P<0.01, AUC=0.893), respectively. The obtained results suggest EOG based analysis is a viable, non-invasive alternative MG screening method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".