A novel adaptive time-window method for detecting slow wave–spindle coupling: Comparison of temporal co-occurrence and phase-amplitude coupling approaches
Bibliographic record
Abstract
BACKGROUND: Slow wave-spindle (SW-SP) coupling is critical to the role of sleep in cognition. However, the reliability and validity of available detection methods remain lacking, employ varying approaches, and have yet to be directly compared and rigorously validated. This study aimed to: (1) compare phase amplitude coupling (PAC)-based methods (PACTools and YASA) with the "temporal co-occurrence", Fixed Time Window approach for detecting SW-SP coupling, and, (2) introduce a refined adaptive SW-SP coupling method to improve precision and accuracy, by precisely aligning spindle detection with slow wave half-wave durations. NEW METHOD: A novel SW-SP coupling detection method was developed, incorporating dynamic time windows based on slow wave half-wave durations. This method adjusts to the morphology of individual slow waves offering precise alignment of spindle events with slow wave peaks and troughs. RESULTS: PAC-based methods showed high sensitivity but low specificity, resulting in excessive false positives. Across PAC indices (MVLMI, KLMI, PLV, GLMMI), the average F1-score was ∼0.45 ± 0.01. YASA, which detects coupling events based on sigma-band oscillations, exhibited moderate accuracy (0.46 ± 0.007), with an F1-score of 0.446 ± 0.011, reflecting its tendency to over-detect events due to reliance on sigma power fluctuations rather than discrete spindles. The Fixed Time Window method demonstrated higher specificity, identifying 533 ± 28 coupled spindles per participant, but relied on static temporal boundaries, leading to an average lag of 1.04 ± 0.01 s relative to slow wave peaks. The Adaptive Half-Wave method improved upon this by dynamically adjusting detection windows to slow wave morphology, reducing the average lag to 0.15 ± 0.007 s while maintaining high specificity (accuracy = 0.83 ± 0.01, precision = 0.96 ± 0.01). COMPARISON WITH EXISTING METHODS: The adaptive method provides a significant improvement in temporal precision and specificity by dynamically aligning detection windows with slow wave morphology. Fully integrated into the Counting Sheep PSG EEG toolbox, it streamlines workflows for spindle detection, slow wave characterization, and coupling analysis within an easy to use, EEGLAB-compatible environment. CONCLUSIONS: As SW-SP coupling gains recognition as an important measure of sleep's role in cognition, the need for a standardized detection method has become clear. The adaptive method provides a robust, open-source solution, addressing the need for standardized SW-SP coupling detection.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".