| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - sleep-staging |
| - eeg |
| - pytorch |
| - physioex |
| pretty_name: PhysioEx Pretrained Models |
| --- |
| |
| # PhysioEx — Pretrained Sleep Staging Models |
|
|
| Pretrained deep learning models for automatic sleep staging from [PhysioEx](https://github.com/guidogagl/physioex) (Gagliardi et al. 2025). |
|
|
| All models follow the AASM 5-class standard: **W, N1, N2, N3, REM**. |
|
|
| ## Available Models |
|
|
| | Model | Architecture | Training Dataset | Channels | Pipeline | Params | Reference | |
| |---|---|---|---|---|---|---| |
| | `seqsleepnet-phan` | BiLSTM + Attention | Sleep-EDF | EEG | seqsleepnet (STFT) | 659K | Phan et al. 2019 | |
| | `tinysleepnet-supratak` | CNN + LSTM | Sleep-EDF | EEG | raw | 83K | Supratak & Guo 2020 | |
| | `sleeptransformer-phan` | Transformer | SHHS | EEG | seqsleepnet (STFT) | 2.1M | Phan et al. 2022 | |
| | `lseqsleepnet-phan` | Long-sequence BiLSTM | SHHS | EEG | seqsleepnet (STFT) | 1.8M | Phan et al. 2023 | |
| | `chambon2018` | Braindecode CNN | MASS SS3 | EEG | raw (128 Hz) | 220K | Chambon et al. 2018 | |
| | `tsinalis-2016` | 2-layer CNN | Sleep-EDF | EEG | identity | 145K | Tsinalis et al. 2016 | |
|
|
| *Models are added as training completes. Check back for updates.* |
|
|
| ## Usage |
|
|
| ```python |
| from physioex.models import load_from_pretrained |
| |
| # Load a pretrained model |
| model = load_from_pretrained("seqsleepnet-phan", verbose=True) |
| |
| # Use for inference or embedding extraction |
| from physioex.models import extract_embeddings, load_embeddings |
| |
| # Extract embeddings on a new dataset |
| path = extract_embeddings(model, dataset, model_name="seqsleepnet-phan", ...) |
| |
| # Or download pre-extracted embeddings from HuggingFace |
| path = load_embeddings("seqsleepnet-phan", "hmc", verbose=True) |
| ``` |
|
|
| ## Cross-Dataset Evaluation (SeqSleepNet-Phan) |
|
|
| Zero-shot transfer from Sleep-EDF to other datasets (voting evaluation): |
|
|
| | Dataset | ACC | Macro F1 | κ | |
| |---|---|---|---| |
| | **Sleep-EDF** (train) | 0.8101 | 0.7575 | 0.7421 | |
| | DCSM | 0.6649 | 0.5839 | 0.5370 | |
| | MESA | 0.6330 | 0.5546 | 0.4921 | |
| | MASS | 0.6033 | 0.5422 | 0.4655 | |
| | HMC | 0.5851 | 0.5574 | 0.4464 | |
| | WSC | 0.4928 | 0.4577 | 0.3255 | |
| | Parkinsons | 0.4428 | 0.3902 | 0.2737 | |
| | SHHS | 0.3989 | 0.3597 | 0.2434 | |
| | Alzheimers | 0.2582 | 0.1449 | 0.0239 | |
|
|
| ## Pre-extracted Embeddings |
|
|
| Contextualized per-epoch embeddings are available as separate dataset repos: |
|
|
| | Model | HuggingFace Repo | Datasets | |
| |---|---|---| |
| | SeqSleepNet-Phan | [`4rooms/seqsleepnet-phan-embeddings`](https://huggingface.co/datasets/4rooms/seqsleepnet-phan-embeddings) | 20 datasets, 12.5K subjects | |
|
|
| ## 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/ | |
| | WSC | NSRR | https://sleepdata.org/datasets/wsc | |
| | MrOS | NSRR | https://sleepdata.org/datasets/mros | |
|
|
| ## 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{phan2019seqsleepnet, |
| title={SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging}, |
| author={Phan, Huy and Andreotti, Fernando and Cooray, Navin and Chen, Oliver Y and De Vos, Maarten}, |
| journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, |
| year={2019}, |
| } |
| |
| @inproceedings{supratak2020tinysleepnet, |
| title={TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG}, |
| author={Supratak, Akara and Guo, Yike}, |
| booktitle={IEEE EMBC}, |
| year={2020}, |
| } |
| |
| @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}, |
| } |
| |
| @article{phan2023lseqsleepnet, |
| title={L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging}, |
| author={Phan, Huy and Ch\'en, Oliver Y and Koch, Philipp and Lu, Zhongxiang and McLoughlin, Ian and Mertins, Alfred and De Vos, Maarten}, |
| journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, |
| year={2023}, |
| } |
| |
| @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}, |
| } |
| |
| @article{tsinalis2016automatic, |
| title={Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks}, |
| author={Tsinalis, Orestis and Matthews, Paul M and Guo, Yike and Zafeiriou, Stefanos}, |
| journal={arXiv:1610.01683}, |
| year={2016}, |
| } |
| ``` |
|
|
