Detection and Classification of Obstructive Sleep Apnea Disorders: A Comparative Analysis of Various Deep Machine Learning Classifiers
Bibliographic record
Abstract
Obstructive Sleep Apnea (OSA) is a respiratory sleep disorder labeled by a temporary cessation of breathing that will last more than 10 seconds while sleeping.Generally, OSA is caused by in-adequate oxygen levels in the human body.It causes daytime fatigue, if goes unchecked results in number of serious health diseases.In general, a Polysomnography (PSG) screening test is used to diagnose Sleep Apnea (SA), but this test is highly expensive, requires constant supervision of a healthcare expert.So, PSG test beyond the reach of general public.In recent years, to overcome these issues, several cost-effective methods are proposed by several healthcare researchers like automatic SA detection methods with the help of emerging Artificial Intelligence (AI).Primary objective of this paper is to analyze the recent advances in SA and review novel approaches, algorithms that have been implemented using AI techniques like ANNs, Machine Learning and Deep Learning.A comprehensive search was performed on many indexing portals, yielding 633 published original research articles and 27 papers.With the promising potential many diagnostic tools, methods are tabulated.Further, how these techniques can be used to detect and diagnose Sleep Apnea in a more convenient way with accuracy as well as economical is discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".