Distinguishing obstructive from central sleep apneas and hypopneas using linear SVM and acoustic features
Bibliographic record
Abstract
Sleep Apnea (SA) is a very common but underdiagnosed respiratory disorder. SA has 2 main types, obstructive and central sleep apnea (OSA and CSA, respectively). The distinction between the 2 types is important for proper clinical management. Our aim in this study was to deploy acoustic analysis of breath sounds to distinguish central from obstructive events. We recorded breath sounds from 29 patients from which 45 segments with obstructive only and 40 segments with central only respiratory events were isolated. Subsequently, 10 acoustic features were extracted and used to identify basic breath sounds: inspiration, expiration, and snoring. A 2nd set of 6 sound-specific features were extracted from the basic sounds, designed based on SA pathophysiology. These 6 features were used to train and test a linear SVM classifier using a leave-one-out cross validation scheme. We achieved an excellent accuracy of 91.8%. In conclusion, this is the first study to demonstrate the ability to distinguish CSA from OSA with high reliability from breath sound recordings during sleep.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".