1120 Measuring Sleep Depth with Odds Ratio Product (ORP) and In-ear EEG Earbuds
Bibliographic record
Abstract
Abstract Introduction Odds Ratio Product (ORP) is a highly validated measure of sleep depth and sleep-wake continuum derived from 3-second EEG epochs using spectral power analysis of the signal. It has been shown to be more sensitive than traditional sleep metrics to detect sleep quality and there is increasing scientific evidence of the ability of ORP to identify patient subgroups of sleep-wake disorders and guide personalized treatment. In-Ear EEG is an evolving method of measuring brain activity using in-ear electrodes. The current investigation assessed the signal quality of in-ear EEG data obtained from a single-channel in-ear EEG device (IDUN Guardian) alongside the frontal EEG using the Cerebra Prodigy to determine whether ORP calculation has the potential to be translated from frontal EEG to in-ear EEG. Methods 18 healthy participants (9 male) were selected ensuring diversity in age (recruited in 24-60 years old range) and sex. Participants wore the Cerebra Prodigy (with forehead electrodes), IDUN Guardian (with in-ear electrodes) while sleeping at home for 3 nights. Data from the devices was collected, synchronized, anonymized, and analyzed to assess the correlation between ORP values derived from the two systems. Results Both in-ear and frontal EEG datasets were pre-processed separately (filtered between 0.3 - 40Hz). No artifact rejection was performed. Pearson correlation coefficient of ORP values was calculated between the two systems. An average correlation of 0.35 was found for raw signals based on 3-second epochs where the in-ear EEG seemed to overestimate ORP values. This was improved (r=0.70) by averaging over 10 ORP values (30 second epoch). This is likely due to masking of variability in signals variability in summary statistics. Conclusion The results showed that single channel in-ear EEG has the potential to resolve ORP values similar to forehead EEG signals. These results suggest the need for an in-depth analysis of the spectral powers in the different frequency ranges in both systems to guide work on improving alignment between the systems. Support (if any) This research work was made possible through the Eureka Eurostars program E2634 grant and was financially supported in Switzerland by Innosuisse - Swiss Innovation Agency and in Canada by the National Research Council Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".