Adaptive Chirplet Transform-Based Sleep State Detection
Bibliographic record
Abstract
Sleep disorders are detrimental to physical and mental health, so accurate diagnostic tools can be of great benefit. We propose an advanced method for analyzing sleep EEG (Electroencephalogram) data using the Adaptive Chirplet Transform (ACT) combined with neural networks. Traditional approaches such Fourier Transform-based methods often assume a signal is periodic, which can miss the detection of time-varying dynamics of brain activity such as sleep spindles. In contrast, the ACT retains important nonstationary information in phase space, allowing for the capture of many transient features. Data from wearable devices, such as the WHOOP and InteraXon Muse headband, were collected to track sleep and EEG waves. The ACT, optimized with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, was then applied to extended EEG data segments to improve accuracy and efficiency. A random forest classifier, with hyperparameter tuning via Grid-SearchCV, was employed to classify sleep stages. This study introduces a new method of sleep stage classification, offering a promising method for understanding sleep, which will hopefully eventually lead to a new way to diagnose sleep disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".