Relationship between Arousal Intensity and Heart Rate Response to Arousal
Why this work is in the frame
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Bibliographic record
Abstract
STUDY OBJECTIVES: The visual appearance of cortical arousals varies considerably, from barely meeting scoring criteria to very intense arousals. Arousal from sleep is associated with an increase in heart rate (HR). Our objective was to quantify the intensity of arousals in an objective manner using the time and frequency characteristics of the electroencephalogram (EEG) and to determine whether HR response to arousal correlates with arousal intensity so determined. DESIGN: Post hoc analysis of 20 preexisting polysomnography (PSG) files. SETTING: Research and Development Laboratory (YRT Limited). PARTICIPANTS: N/A. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Arousals were scored using the American Academy of Sleep Medicine criteria. The EEG signals' time and frequency characteristics were determined using wavelet analysis. An automatic algorithm was developed to scale arousal intensity based on the change in wavelet features and data from a training set obtained from 271 arousals visually scaled between zero and nine (most intense). There were 2,695 arousals in 20 PSGs that were scaled. HR response (ΔHR) was defined as the difference between the highest HR in the interval [arousal-onset to (arousal-end +8 sec)] and the highest HR between 2 and 12 sec preceding arousal onset. There was a strong correlation between arousal scale and ΔHR within each subject (average r: 0.95 ± 0.04). The slope of the relationship varied among subjects (0.7-2.4 min(-1)/unit scale). CONCLUSIONS: Arousal intensity, quantified by wavelet transform, is strongly associated with arousal-related tachycardia, and the gain of the relationship varies among subjects. Quantifying arousal intensity in PSGs provides additional information that may be clinically relevant.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it