Classification of Central and Obstructive Sleep Apnea Using Respiratory Inductance Plethysmography and Convolutional Neural Networks
Bibliographic record
Abstract
In this study, we employ a pre-trained Convolutional Neural Network (CNN), specifically ResNetl8, for automated sleep apnea classification using spectral images derived from Respiratory Inductance Plethysmography (RIP) signals. Using data from 130 patients who underwent diagnostic polysomnogram (PSG) at the Sleep Disorders Clinic of the Royal Ottawa Mental Health Centre, we compare the performance of three models fine-tuned using spectrograms of 60 sec segments of the RIPsum, RIPflow, and the concurrent thoracic (THO) and abdominal (ABD) RIP signals. In the case of the THO/ABD model, concurrent spectrograms of the THO and ABD signals are combined into a single image. The resulting models classified any apnea events (central and obstructive apnea/hypopnea) from normal respiration accuracy ranging from 87-90% and F1 scores ranging 90–91 %. We also examine the models' performance on classification of central sleep apnea, obstructive sleep apnea, and normal respiration. The THO/ ABD model classified central apnea events with 89.4% precision, 90.2% recall, 89.8% F1 score and 94.4% specificity. Our results show that RIP belts can be used as an effective screening tool for sleep apnea, and central sleep apnea more specifically, utilizing smaller datasets and minimal training. This approach may be useful for non-invasive, cost-effective diagnostic methodologies at home and in clinical environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".