Chin EMG analysis for REM sleep behavior disorders
Bibliographic record
Abstract
Many sleep disorders not only diminish daytime performance, increase sleepiness, and affect mood of an individual, but can lead to serious consequences such as: high blood pressure, cardiovascular diseases, stroke and even death. The analysis of chin electromyography (EMG) usually reflects an inhibitory influence on motor activity and muscle tone. Based on literature, it has been reported that the level of chin EMG drops to its lowest level during REM between normal and there is an increase in amplitude in rapid eye movement behavior disorder (RBD) cohorts. In this work, a research study is conducted across eight subjects, to evaluate chin EMG during rapid eye movement (REM) sleep cycle and to measure the strength of muscle activation. The obtained results show that there is a significant increase in RMS value of chin EMG in RBD in comparison to healthy subjects, i.e. RMS value for RBD is at least 2.5 times the normal. Further, the change in regularity among healthy and RBD subjects is investigated for facilitating efficient characterization of sleep-related disorders. Such an analysis could be targeted towards providing a useful objective measure for assessing response to neuroprotective drugs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".