Comparative Analysis of Spectral and Bispectral Analyses for Obstructive Sleep Apnea Severity Classification
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by recurrent episodes of upper airway obstruction during sleep. Accurate and timely diagnosis, along with severity classification, is crucial for effective management and preventing associated health complications. This paper presents a comparative analysis of power spectrum and bispectrum features extracted from physiological signals for the classification of OSA severity. Utilizing a comprehensive dataset and a robust 5-fold stratified cross-validation approach, we evaluate the discriminative power of these features through statistical tests (t-test, ranksum), effect size (Cohen's d), and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) analysis. Furthermore, top-N feature selection and correlation heatmaps are employed to identify the most salient and non-redundant features. Our findings indicate that power spectral features achieved average AUCs between 0.65 and 0.83, while bispectral features, when used alone, modestly outperformed spectral features, with AUC increases of approximately 1–3% across most binary classification tasks. For example, in the Non-OSA vs. Severe-OSA comparison, bispectral features yielded a maximum AUC of 0.84 compared to 0.83 for spectral features, with a corresponding effect size increase of 0.27. These results highlight that while the gains are modest, bispectral analysis provides complementary information beyond power spectrum features by capturing nonlinear interactions. This suggests that a combined approach may enhance robustness for OSA severity classification.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".