Automatic Sleep Stage Classification Using Deep Learning Algorithm for Multi-Institutional Database
Bibliographic record
Abstract
Recent deep learning studies for sleep stage classification with polysomnography (PSG) data show two directions, either using 1-dimensional (1-D) raw PSG data or spectrogram images time-frequency domain. We propose a novel approach using images generated from time-signal display of a PSG dataset for 5 class sleep stage classification. The motivation of our approach is not only to imitate the way used by human sleep-scoring experts but also to make use of various methods developed in image classification in Deep Learning, such as augmentation techniques, EfficientNet and LSTM. In addition an explainable AI technique such as Class Activation Map (CAM) can be employed for interpreting how a model makes a decision. We, also, work on “inconsistency” problems occurring among multiple institutions/hospitals where different capturing sensors are used and the labelling mismatch by human experts in different organizations. To solve the problem, we experiment three different approaches in the network design with data of two institutes and 5 sleep stage classification; (i) 5 class classification, (ii) 10-class classification and then post-processing to 5 classes (iii) 10-to-5 class classification. The 10-to-5 class classification is a network where information of two institutes are embedded inside the network. When information of multi institution is inside the network, the results show higher performance. Our experimental results show that all of three proposed methods based on time-signal images achieves higher accuracy performance compared to state-of-the-art models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".