Obstructive Sleep Apnea Severity Multiclass Classification Using Analysis of Snoring Sounds
Bibliographic record
Abstract
The current gold standard for diagnosing obstructive sleep apnea (OSA) is an overnight multi-channel polysomnography (PSG), an expensive, labour-intensive, and uncomfortable procedure. Accordingly, it would be beneficial to have a screening method to promptly determine the severity of a patient, prior to a referral for PSG. This paper intends to distinguish the severity of OSA patients. We show that an accurate multiclass classification of snoring subjects with four classes of OSA, can be achieved on the sound spectrum of snoring without any information requirement on the number of apneas. 33 Snoring sounds with different degrees of obstructive sleep apnea and non-OSA were analyzed. The proposed technique uses K-Means clustering to cluster the sound spectrum and reconstruct features. Support vector machine (SVM) has been used for the classification. The multiclass snore sounds classification approves early stratification of subjects according to their severity. A classification accuracy of 75.76% was reported using the proposed method. The experimental results also demonstrate that the proposed method can provide diagnostic suggestions for OSA screening.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".