Wavelet-based segmentation techniques in the detection of microarousals in the sleep EEG
Bibliographic record
Abstract
The presence of undesirable frequency bursts called microarousals (MA), within any stage of sleep, causes a medical condition known as excessive daytime sleepiness (EDS). Traditionally, using the electroencephalogram (EEG) and the electromyogram (EMG), a sleep technologist detects the MAs. To reduce the time, cost and errors associated with manual scoring, a three-stage computerized automatic detection procedure is proposed. The first stage involves spectral decomposition using the discrete wavelet transform (DWT). The second stage uses three different segmentation techniques: the autocorrelation function (ACF), the nonlinear energy operator (NLEO) and the generalized likelihood ratio (GLR) methods to segment the detail function into stationary segments. The third stage scores the MAs, by comparing the power and spectral content of each stationary segment with the rules established by Rechtschaffen and Kales. The procedure is applied to two cases: to one EEG channel, in one case, and to a combination of the EEG and EMG channels, in the other. The results show that the presence of the DWT significantly improves the correct MA detection while the combined channel provides the most impressive detection results.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".