Bispectral analysis of tracheal breath sounds for Obstructive Sleep Apnea
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) is a respiratory disorder with serious consequences that is characterized by repetitive cessation of breathing for more than 10s often associated with a drop of more than 4% in the blood's Oxygen saturation level. The gold standard for OSA diagnosis is full-night Polysomnography (PSG), which is a time-consuming, inconvenient, and costly assessment. On the other hand, our team has showed that the analysis of tracheal respiratory sounds recorded during wakefulness holds promises to be used as a simple and effective tool for screening moderate and severe OSA. In this paper, we examine the nonlinear characteristics of tracheal breath sounds and the possibility to extract features from Higher Order Spectra (HOS) for OSA screening. The data used in this study were recorded during wakefulness in two body positions, supine and upright, and during mouth and nose breathing. We estimated the bispectrum of the sounds in each respiratory cycle, calculated the median bifrequencies and the energy of the bispectrum, and investigated any statistically significant differences between the extracted features in two groups of non-OSA and severe OSA data. The differences in the features between body positions and nose/mouth breathing were also looked at. One-way ANOVA revealed significant differences in the features between non-OSA individuals and those with severe OSA. The results encourage the use of these features in future studies for OSA screening.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".