MPSleepNet: Matrix Profile-Guided Transformer for Multi-Channel Sleep Classification
Bibliographic record
Abstract
Polysomnography (PSG)-based sleep staging is vital for assessing sleep quality. As a state-of-the-art deep learning model structure, Transformer has been increasingly utilized to enhance sleep stage classification efficiency. However, the default self-attention mechanism in Transformer encoder often struggles with long-term dependencies in time sequences. To overcome this limitation, we introduce a novel method that integrates the Matrix Profile, a time series data mining technique, with the Transformer's self-attention mechanism to better capture global data abnormalities and temporal dynamics. Our approach preprocesses PSG data from multiple channels using Matrix Profiles to highlight significant patterns. These profiles, combined with time-frequency representations, are fed into a Transformer encoder to fuse multi-channel features, and enhance the understanding of temporal interdependencies. The enhanced features are classified via two fully connected layers, yielding substantial improvements in sleep stage classification accuracy and efficiency on multichannel data as compared to the baseline models. This work not only underscores the efficacy of Matrix Profile in sleep classification but also proposes a new paradigm for processing complex temporal data in deep learning models. The complete code for this model is available at https://github.com/EliteriaLYU/MPSleepNet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".