Determining levels of arousal using electrocardiography: A study of HRV during transcranial magnetic stimulation
Bibliographic record
Abstract
Introduction. Electrocardiography (ECG) can be used to collect heart rate data which in turn can be used to measure heart rate variability (HRV). The sympathetic and parasympathetic nervous systems have the ability to affect this variability, which can ultimately affect arousal levels. Background. This study was designed to determine if changes to HRV can be detected while subjects undergo transcranial magnetic stimulation (TMS), and if these changes are from decreased arousal. Methods. LabChart™ software was utilized to collect and analyze initial heart rate data to generate R-R intervals. Further analysis was then performed in order to determine RRV3 and RRV8-3. Results. Low arousal levels were detected in 5 of the 6 analyses, demonstrating that the subjects' arousal levels changed throughout the experiment. Conclusion. HRV effectively measures arousal levels. Future research should be done involving continuous collection of heart rate during TMS to allow for a cross comparison between time of low arousal states and potential changes to TMS data.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".