0120 Emulating Human Sleep Spindle Scoring
Bibliographic record
Abstract
Sleep spindles are a marker of stage 2 NREM sleep, have been linked to the process of learning & memory, and are altered by many neurological diseases. For human clinical polysomnography, the visual scoring of sleep spindles by human experts is generally considered the gold standard, but it is time-consuming, costly and can introduce inter/ra-scorer bias. Automated spindle detection methods are efficient and reproducible, but are not well-correlated with human scoring. Typically, automated detectors find large numbers of false positives (‘hidden spindles’) relative to human scorers. While it is plausible that the false positives are biologically meaningful, these ‘hidden spindles’ present several problems, including: i) Lack of gold standard for ‘hidden spindles’; ii) Lack of agreement between automated detectors for ‘hidden spindles’; iii) ‘Hidden spindles’ can be found throughout NREM, REM and wake, and therefore no longer are consistent with the original concept of the sleep spindle. To reduce the problem of ‘hidden spindles’, we have developed an automated spindle detector (‘A7’) that emulates how a human scores spindles. The ‘A7’ detector relies on the correlation/covariation of the sigma band-passed signal to the original broadband filtered (0.3-30Hz) EEG signal. To test the performance of the detector, we compared it against a gold standard spindle dataset derived from the consensus of a crowd-sourced group of human experts. The by-event performance of the ‘A7’ spindle detector was similar to individual experts (f1 score: 0.70 vs 0.67) against the consensus of a group of human experts. This was 0.17 points higher than other spindle detectors we tested. The ‘A7’ detector is designed to emulate human spindle scoring by minimizing the number of ‘hidden spindles’ detected and thereby detecting spindles that have the highest signal/noise ratio. We provide an open-source implementation of this detector for further use and testing. Funding for this work was provided by the Chaire Pfizer, Bristol-Myers Squibb, SmithKline Beecham, Eli Lilly en psychopharmacologie de l’ Université de Montréal, the Centre de Recherche Hôpital du Sacré-Coeur de Montréal, and the Canadian Institutes of Health Research (CIHR).
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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) | 0.006 | 0.005 |
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; both teacher heads agree on what is shown here.
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".