Effect of obstructive sleep apnea treatment on renal function in patients with cardiovascular disease
Bibliographic record
Abstract
Introduction: Previous studies have considered using the heart rate variability (HRV) and ECG derived respiration (EDR) as inputs for an ECG based apnoea detection system and accuracies over 85% have been achieved for detecting epochs containing obstructive sleep apnoea (OSA) events. The physiological influences on the heart rate and respiration are not independent. Cardiopulmonary coupling (CPC) measures the coordination between cardiac and respiratory systems as determined from the ECG and has proven to be a useful feature for identifying sleep stages. We investigated supplementing the HRV and EDR with CPC information, and investigated the impact on classifying one-minute epochs for the presence or absence of OSA events. Machine learning methods were used to develop fully automated analysis algorithms Methods: We used the 35 ECG recordings extracted from scored overnight polysomnography recordings with an average recording time of 8 hours. Scoring of the PSGS included respiratory and sleep stage events. By processing the ECG with signal processing algorithms, the HRV, the EDR (using QRS area method) and the CPC information was determined. Key measurements from the HRV, EDR and CPC were then used as inputs to an artificial neural network classifier which was trained with the back-propagation algorithm. Unbiased performance was assessed with leave-onerecord-out cross-validation. Results: The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset for determining the presence or absence of OSA, were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. Discussion: The CPC information captured valuable sleep stage information that assisted the performance of fully automatic algorithms for detecting OSA events from the ECG.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".