Tree-Based Machine Learning Techniques for Automated Human Sleep Stage Classification
Bibliographic record
Abstract
Background: Sleep disorders pose significant health risks, necessitating accurate diagnostics.The analysis of polysomnographic data and subsequent sleep stage classification by medical professionals are crucial in diagnosing these disorders.The application of artificial intelligence (AI)-based systems for automated sleep stage classification has gained significant momentum recently.Methodology: In this study, we introduce a machine learning model designed for high-accuracy, automated sleep stage classification.We utilized a dataset consisting of polysomnographic data from 50 individuals, obtained from the Yozgat Bozok University Sleep Center.A variety of classifiers, including Extra Tree, Decision Tree, Random Forest, Ada Boost, and Gradient Boost, were tested.Sleep stages were classified into three categories: Wakefulness (WK), Rapid Eye Movement (REM), and Non-Rapid Eye Movement (N-REM).Results: The overall classification accuracies were 95.4%, 95%, and 92% for three distinct classifiers, respectively, with the highest accuracy reaching 98.8%.Comparison with Existing Methods: This study distinguishes itself from comparable sleep stage-scoring research by utilizing a unique dataset, and by incorporating data from 16 channels, which contributes to the achieved accuracy.Conclusion: The machine learning model trained with a unique dataset demonstrated high classification success in the automated scoring of sleep stages.This research underscores the potential of machine learning techniques in improving sleep disorder diagnostics.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".