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language: en
license: apache-2.0
tags:
- sleep-staging
- eeg
- embeddings
- physioex
- chambon2018
pretty_name: Chambon2018 Embeddings
size_categories:
- 1K<n<10K
---
# Chambon2018 Embeddings
Pre-extracted per-epoch embeddings from **Chambon2018** (Chambon et al. 2018),
trained on MASS SS3 via [PhysioEx](https://github.com/guidogagl/physioex).
Each subject directory contains:
- `embeddings.npy` — `(n_epochs, 120)` per-epoch embeddings (bfloat16)
- `labels.npy` — `(n_epochs,)` AASM sleep stage labels
**Note:** Chambon2018 is a per-epoch CNN encoder (no inter-epoch context).
Embeddings are extracted directly without sliding-window voting.
## Usage
```python
from physioex.models import load_embeddings
path = load_embeddings("chambon2018", "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 |
|---|---|---|---|---|---|---|---|---|---|
| mass_ss05 | 26 | **0.8146** | **0.6653** | **0.7284** | 0.74 | 0.08 | 0.88 | 0.83 | 0.80 |
| mass_ss02 | 19 | 0.7902 | 0.6397 | 0.6919 | 0.74 | 0.05 | 0.86 | 0.79 | 0.75 |
| mass_ss03 | 62 | 0.7869 | 0.6638 | 0.6806 | 0.79 | 0.15 | 0.87 | 0.75 | 0.76 |
| mass_ss04 | 40 | 0.7777 | 0.6353 | 0.6785 | 0.73 | 0.06 | 0.87 | 0.78 | 0.75 |
| dcsm | 255 | 0.7218 | 0.5239 | 0.5706 | 0.82 | 0.00 | 0.74 | 0.72 | 0.34 |
| mass_ss01 | 53 | 0.7134 | 0.5899 | 0.5882 | 0.81 | 0.23 | 0.82 | 0.48 | 0.61 |
| sleepedf | 153 | 0.6001 | 0.4502 | 0.4198 | 0.65 | 0.00 | 0.72 | 0.52 | 0.36 |
| hmc | 151 | 0.5980 | 0.5099 | 0.4581 | 0.66 | 0.03 | 0.65 | 0.67 | 0.54 |
*SHHS, MESA, HPAP, STAGES results pending (extraction running on Sofia HPC).*
## Model Details
- **Architecture**: Chambon2018 (Chambon et al. 2018) — braindecode SleepStagerChambon2018 per-epoch CNN
- **Training data**: MASS SS3 (62 subjects, single EEG channel at 128 Hz)
- **Pipeline**: `raw` (bandpass 0.3-40 Hz, resample 128 Hz)
- **Sequence length**: L=3 (central epoch classification)
- **Embedding dim**: 120
## 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 |
| 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{chambon2018deep,
title={A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series},
author={Chambon, Stanislas and Galtier, Mathieu and Arnal, Pierrick and Wainrib, Gilles and Gramfort, Alexandre},
journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
year={2018},
}
```
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