A Smart Mandibular Device for Intra-oral Electroencephalogram Monitoring
Bibliographic record
Abstract
Electroencaphalography (EEG) is one of the vital signs monitored during sleep study for patients suffering from obstructive sleep apnea disorder (OSA). OSA is a condition where patients suffer from irregular sleep patterns due to difficulty in breathing. A possible treatment of OSA is use of mandibular advancement devices (MADs). In this manuscript, we present a smart MAD that can monitor intraoral EEG signal. The device acquires intra-oral EEG signal from the mouth-roof. So far very limited research is done in the area of intra-oral EEG monitoring. The proposed smart MAD consists of a MAD, three flexible gold EEG electrodes and an EEG measurement system implemented on a flexible polymide substrate. The measurement system is battery operated and sends EEG data wirelessly using a BLE 5.0 transceiver. The system is validated by acquiring intra-oral EEG signal for "eye-open" and "eye-close" activities of five volunteers. The frequency domain analysis of the intra-oral EEG signal clearly shows that "eye-open" and "eye-close" activities can be detected from the data. In future, this smart MAD will be used to acquire intra-oral EEG data during sleep for OSA patients and determine sleep stages.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".