Smart Mandibular Advancement Device for Intraoral Monitoring of Cardiorespiratory Parameters and Sleeping Postures
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Bibliographic record
Abstract
Obstructive sleep apnea (OSA), as a highly prevalent sleep disorder, causes several serious health complaints. It has been proved that using intraoral mandibular advancement devices (MADs) during sleep is an efficient treatment for OSA. However, due to limited number of sleep study laboratories, effectiveness of MAD therapy is not regularly monitored. This paper proposes a smart MAD with the capability of continuously monitoring of cardiorespiratory parameters as well as sleeping postures and breathing routes. In this regard, a flexible hybrid wireless sensing platform based on the intraoral photoplethysmography (PPG), temperature and accelerometry monitoring is developed. It is qualitatively and quantitatively discussed that the intraorally captured PPG signals by the smart MAD have similar features as the ones received from the conventional anatomical position, i.e., the left index fingertip. Extensive experimental measurements indicate that the proposed smart MAD can estimate heart-rate (HR), respiration rate (RR) and blood oxygen saturation (SpO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) with the maximum mean-absolute-errors of 2.4 bpm, 2.52 breaths/min, and 0.8%, respectively, in comparison to the reference measurements, while such a capability is not dependent on subject's positions and breathing routes. It is also shown that the smart MAD can readily identify different sleeping postures, namely, supine, left, right, and prone and breathing routes. The reliability and stability of the proposed smart MAD's measurements are proved by examining a group of subjects. The proposed smart MAD has potential to monitor the effectiveness of MAD treatment and eliminate untreated OSA without the requirement of attaching an extra monitoring platform to the patient's body.
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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 | 0.000 | 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 it