Bibliographic record
Abstract
Measures of sleep physiology, not obvious to the human eye, may provide important clues to disease states, and responses to therapy. A significant amount of eye movement data is not attended to clinically in routine sleep studies because these data are too long, about six to eight hours in duration, and they are also mixed with many unknown artifacts usually produced from EEG signals or other activities. This research describes how eye movements were different in depressed patients who used antidepressant medications, compared to those who did not. The goal is to track antidepressant medications effects on sleep eye movements. Clinically used SSRIs such as Prozac (Fluoxetine), Celexa (Citalopram), Zoloft (Sertraline), the SNRI Effexor (Venlafaxine) have been considered in this study to assess the possible connections between eye movements recorded during sleep and serotonin activities. The novelty of this research is in the assessment of sleep eye movement, in order to track the antidepressant medications' effect on the brain through EOG channels. EOG analysis is valuable because it is a noninvasive method, and the following research is looking for findings that are invisible to the eyes of professional clinicians. This thesis focuses on quantifying sleep eye movements, with two techniques: autoregressive modeling and wavelet analysis. The eye movement detection software (EMDS) with more than 1500 lines was developed for detecting sleep eye movements. AR coefficients were derived from the sleep eye movements of the patients who were exposed to antidepressant medications, and those who were not, and then they are classified by means of linear discriminant analysis. also for wavelet analysis, discrete wavelet coefficients have been used for classifying sleep eye movements of the patients who were exposed to medication and those who were not.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".