Unobtrusive Screening of Central Sleep Apnea From Pressure Sensors Measurements: A Patient-Specific Longitudinal Study
Bibliographic record
Abstract
Historically, the lack of patients' sleep histories has caused low identification of sleep apnea (SA) and referral rates. Moreover, the costly and time-consuming nature of polysomnography (PSG) as a standard clinical test for detecting SA and the lack of sleep clinics has created a demand for suitable home-based monitoring devices. Pressure measurement using a pressure sensitive mat (PSM) can address the challenges found in current sleep-monitoring solutions. The noncontact PSM has a potential to replace obtrusive breathing sensors in the sleep lab and to be used as a prescreening tool for patients suspected of having SA. Applying classical support vector machine (SVM), this article presents a personalized system based on the measurements of each patient to detect central SA (CSA) events and monitor sleep characteristics longitudinally. For this purpose, sensor set-ups were installed in nine seniors' homes to collect unsupervised pressure data in approximately one year ranging from 8 to 12 months. Cost-based and resampling-based approaches were examined to combat imbalanced data. The results showed that the cost-based method outperformed other methods. Next, the patient-specific system was used to determine the total number of CSA events, as well as their starting time and duration in each day. The SA severity was measured by the central apnea index (CAI). In addition, other sleep characteristics such as bed occupancy (BO), day clock, and night clock were extracted from the PSM measurements. The impact of longitudinal sleep monitoring could be in tracking SA treatment progression, and possibly providing information on the interaction between SA and other disease progressions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".