A Time-Series Approach to Predict Obstructive Sleep Apnea (OSA) Episodes
Bibliographic record
Abstract
Sleep apnea is a common respiratory disorder during sleep.It is characterized by pauses in breathing or shallow breathing during sleep for longer than 10 seconds.Except the fact that not having a proper sleep and being rested for the next day, in some cases the apnea period (not breathing interval) may last more than 30 seconds and this situation can even be fatal.14% of men and 5% of women suffer from Obstructive Sleep Apnea (OSA) in United States.Patients may experience apnea for more than 300 times in a single night sleep.Polysomnography (PSG) is a multi-parametric recording of biophysiological changes, containing EEG, ECG, SpO2, Nasal Airflow signals, performed during overnight sleep.In this study, a fully automatic apnea detection algorithm is developed and an early warning system is proposed to predict OSA episodes by extracting time-series features of OSA periods and regular respiration using nasal airflow signal.Extracted features are then reduced to improve the performance of the prediction.Support vector machines (SVM), one of the commonly used classification algorithms in medical applications, is implemented for learning and prediction of the OSA episodes.The results show that OSA episodes are predicted with 87.6% of accuracy and 91.3% of sensitivity, 30 seconds before patient faces apnea.By this approach, apnea related health risks can be minimized by foreknowledge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".