A robust two-stage sleep spindle detection approach using single-channel EEG
Bibliographic record
Abstract
Abstract Objective. Sleep spindles in the electroencephalogram (EEG) are significant in sleep analysis related to cognitive functions and neurological diseases, and thus are of great clinical interests. An automatic sleep spindle detection algorithm could help decrease the workload of visual inspection by sleep clinicians. Approach. We propose a robust two-stage approach for sleep spindle detection using single-channel EEG. In the pre-detection stage, a stable number of sleep spindle candidates are discovered using the Teager energy operator with adaptive parameters, where the number of true sleep spindles are ensured as many as possible to maximize the detection sensitivity. In the refinement stage, representative features are designed and a bagging classifier is exploited to further recognize the true spindles from all candidates, in order to remove the false detection in the first stage. Main results. Using the union of all experts’ annotations as the ground truth, its performance outperforms state-of-the-art works in terms of F1-score (F1) on two public databases (F1: 0.814 for Montreal archive of sleep studies dataset and 0.690 for DREAMS dataset). The annotation consistency between the proposed method and certain selected expert as the trainer could exceed the consistency between two human experts. Significance. The proposed sleep spindle detection method is based on single-channel EEG thus introduces as less interference to the subjects as possible. It is robust to subject variations between databases and is capable of learning certain annotation rules, which is expected to help facilitate the manual labeling of certain experts. In addition, this method is fast enough for real-time applications.
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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".