| --- |
| 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}, |
| } |
| ``` |
| |
