README.md

---
language: en
license: apache-2.0
tags:
  - sleep-staging
  - eeg
  - embeddings
  - physioex
  - sleeptransformer
pretty_name: SleepTransformer-Phan Embeddings
size_categories:
  - 10K<n<100K
---

# SleepTransformer-Phan Embeddings

Pre-extracted contextualized per-epoch embeddings from **SleepTransformer** (Phan et al. 2022),
trained on SHHS via [PhysioEx](https://github.com/guidogagl/physioex).

Each subject directory contains:
- `embeddings.npy` — `(n_epochs, 128)` contextualized epoch embeddings (bfloat16)
- `labels.npy` — `(n_epochs,)` AASM sleep stage labels

## Usage

```python
from physioex.models import load_embeddings
path = load_embeddings("sleeptransformer-phan", "hmc", verbose=True)
```

## Linear Probe Results (5-fold subject-wise CV)

| Dataset | Subjects | ACC | MF1 | κ | F1-W | F1-N1 | F1-N2 | F1-N3 | F1-REM |
|---|---|---|---|---|---|---|---|---|---|
| shhs_visit1 | 5793 | **0.8861** | **0.8197** | **0.8380** | 0.92 | 0.51 | 0.90 | 0.85 | 0.91 |
| shhs_visit2 | 2651 | 0.8824 | 0.8068 | 0.8336 | 0.92 | 0.47 | 0.90 | 0.85 | 0.90 |
| dcsm | 255 | 0.8767 | 0.7855 | 0.8202 | 0.95 | 0.45 | 0.84 | 0.83 | 0.85 |
| mass_ss05 | 26 | 0.8750 | 0.8138 | 0.8183 | 0.85 | 0.55 | 0.90 | 0.87 | 0.89 |
| mass_ss02 | 19 | 0.8714 | 0.8065 | 0.8111 | 0.83 | 0.55 | 0.92 | 0.87 | 0.87 |
| mass_ss04 | 40 | 0.8638 | 0.8124 | 0.8048 | 0.85 | 0.58 | 0.91 | 0.85 | 0.88 |
| mass_ss03 | 62 | 0.8596 | 0.8063 | 0.7918 | 0.86 | 0.56 | 0.90 | 0.82 | 0.89 |
| sleepedf | 153 | 0.8394 | 0.7828 | 0.7770 | 0.93 | 0.50 | 0.86 | 0.81 | 0.82 |
| mass_ss01 | 53 | 0.8234 | 0.7741 | 0.7503 | 0.91 | 0.53 | 0.87 | 0.70 | 0.87 |
| stages_GSDV | 232 | 0.8167 | 0.6701 | 0.7015 | 0.84 | 0.24 | 0.87 | 0.58 | 0.82 |
| hpap | 247 | 0.8150 | 0.7755 | 0.7476 | 0.88 | 0.48 | 0.84 | 0.80 | 0.87 |
| stages_GSBB | 30 | 0.8124 | 0.7168 | 0.7149 | 0.90 | 0.37 | 0.85 | 0.66 | 0.81 |
| stages_MSMI | 63 | 0.7974 | 0.7276 | 0.6967 | 0.83 | 0.37 | 0.85 | 0.75 | 0.84 |
| stages_GSSW | 105 | 0.7908 | 0.6312 | 0.6617 | 0.79 | 0.18 | 0.86 | 0.50 | 0.83 |
| stages_GSLH | 45 | 0.7902 | 0.6714 | 0.6688 | 0.85 | 0.36 | 0.85 | 0.54 | 0.77 |
| stages_MSQW | 153 | 0.7839 | 0.7064 | 0.6817 | 0.82 | 0.47 | 0.85 | 0.56 | 0.83 |
| hmc | 151 | 0.7836 | 0.7532 | 0.7141 | 0.84 | 0.46 | 0.80 | 0.83 | 0.83 |
| mesa | 2056 | 0.7772 | 0.6932 | 0.6810 | 0.84 | 0.40 | 0.82 | 0.61 | 0.79 |
| stages_GSSA | 26 | 0.7767 | 0.5691 | 0.6179 | 0.75 | 0.10 | 0.84 | 0.38 | 0.77 |
| stages_STLK | 158 | 0.7737 | 0.6601 | 0.6563 | 0.80 | 0.33 | 0.83 | 0.52 | 0.82 |
| stages_STNF | 460 | 0.6409 | 0.5803 | 0.5182 | 0.71 | 0.18 | 0.65 | 0.74 | 0.61 |
| stages_MSTR | 285 | 0.5862 | 0.4573 | 0.3601 | 0.51 | 0.05 | 0.69 | 0.52 | 0.51 |
| stages_MSNF | 38 | 0.5448 | 0.4035 | 0.2983 | 0.55 | 0.10 | 0.66 | 0.38 | 0.33 |
| stages_BOGN | 85 | 0.5311 | 0.4027 | 0.2819 | 0.49 | 0.14 | 0.64 | 0.34 | 0.39 |
| stages_MSTH | 31 | 0.4917 | 0.2057 | 0.0492 | 0.22 | 0.00 | 0.66 | 0.09 | 0.06 |

## Model Details

- **Architecture**: SleepTransformer (Phan et al. 2022) — epoch transformer + sequence transformer
- **Training data**: SHHS visit 1 (5793 subjects, single EEG channel)
- **Pipeline**: `seqsleepnet` (STFT spectrogram, T=29, F=129)
- **Sequence length**: L=21 epochs
- **Embedding dim**: 128

## Datasets

| Dataset | Source | URL |
|---|---|---|
| Sleep-EDF | PhysioNet | https://physionet.org/content/sleep-edfx/1.0.0/ |
| HMC | PhysioNet | https://physionet.org/content/hmc-sleep-staging/1.1/ |
| DCSM | ERDA/KU | https://erda.ku.dk/public/archives/db553715ecbe1f3ac66c1dc569826eef/published-archive.html |
| SHHS | NSRR | https://sleepdata.org/datasets/shhs |
| MESA | NSRR | https://sleepdata.org/datasets/mesa |
| HomePAP | NSRR | https://sleepdata.org/datasets/homepap |
| STAGES | NSRR | https://sleepdata.org/datasets/stages |
| MASS | CEAMS | http://ceams-carsm.ca/mass/ |

## Citations

```bibtex
@article{gagliardi2025physioex,
    author={Gagliardi, Guido and Alfeo, Luca and Cimino, Mario G C A and Valenza, Gaetano and De Vos, Maarten},
    title={PhysioEx, a new Python library for explainable sleep staging through deep learning},
    journal={Physiological Measurement},
    url={http://iopscience.iop.org/article/10.1088/1361-6579/adaf73},
    year={2025},
}

@article{phan2022sleeptransformer,
    title={SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification},
    author={Phan, Huy and Mikkelsen, Kaare and Ch\'en, Oliver Y and Koch, Philipp and Mertins, Alfred and De Vos, Maarten},
    journal={IEEE Transactions on Biomedical Engineering},
    year={2022},
}
```