Screening Sleep Apnea Using Random Convolution Kernels of Acoustic Speech Representations
Bibliographic record
Abstract
Obstructive sleep apnea (OSA), characterized by complete or partial airway obstruction during sleep, affects approximately 38% of the adult population. The clinical diagnosis of OSA is typically conducted using polysomnography (PSG), which has been reported to be cumbersome, requires technical expertise, and involves long waitlists. Consequently, alternative methods, such as questionnaires, have gained attention in recent years. However, while these methods are highly sensitive, they suffer from low specificity. Speech-based screening has emerged as an accessible alternative and has been widely explored in the literature. Despite this, existing technologies primarily rely on acoustic features and classical machine learning models, with the potential of deep neural networks largely unexplored. In this study, we employed random convolution kernels to simulate the effects of convolutional neural networks on speech acoustic features to predict the severity of sleep apnea based on two apnea-hypopnea index (AHI) thresholds: 10 and 15 events/hour. Our results, based on a sample of 35 individuals, achieved an F1-score of 0.83 for the 10 events/hour threshold and 0.71 for the 15 events/hour threshold, surpassing the performance reported in the literature with similar sample sizes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".