Optimal CNN Model for Obstructive Sleep Apnea Detection using Particle Swarm Optimization
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder with significant health risks. It is characterized by the narrowing of the upper airway during sleep, leading to vibrations in the airway structures and the production of snoring sounds. Recently, Convolutional Neural Networks (CNNs) have been leveraged to extract meaningful features from snoring sound data, enabling early and accurate detection of OSA. The effectiveness of these neural network optimizations depends on the starting values of the model, the gradient algorithm used, and the complexity of the problem. This study introduces an improved Particle Swarm Optimization (PSO) strategy that linearly adjusts the learning rate coefficient to enhance accuracy and convergence speed. Our approach was evaluated on a collected and pre-processed dataset based on the PSG-Audio database. Experimental results demonstrate that our method significantly outperforms the conventional optimization algorithm and existing PSO techniques, achieving a remarkable accuracy of 99.1%. These findings confirm the potential of our optimized model for OSA detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".