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README.md

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+---
+license: apache-2.0
+tags:
+  - sleep-staging
+  - cardio-respiratory
+  - non-causal
+  - wav2sleep
+  - polysomnography
+  - time-series
+  - pytorch
+library_name: wav2sleep
+pipeline_tag: time-series-classification
+---
+
+# wav2sleep
+
+Cardio-respiratory sleep staging (4-class: Wake, Light, Deep, REM)
+
+## Model Description
+
+This is a **wav2sleep** model for automatic sleep stage classification from cardio-respiratory signals (ECG, PPG, respiratory).
+wav2sleep is a unified multi-modal deep learning approach that can process various combinations
+of physiological signals for sleep staging.
+
+- **Paper**: [wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification](https://arxiv.org/abs/2411.04644)
+- **Repository**: [GitHub](https://github.com/joncarter1/wav2sleep)
+- **Conference**: ML4H 2024
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Input Signals** | ECG, PPG, ABD, THX |
+| **Output Classes** | 4 |
+| **Architecture** | Non-causal (bidirectional) |
+
+### Signal Specifications
+
+| Signal | Samples per 30s epoch |
+|--------|----------------------|
+| ECG, PPG | 1,024 |
+| ABD, THX | 256 |
+| EOG-L, EOG-R | 4,096 |
+
+## Usage
+
+```python
+from wav2sleep import load_model
+
+# Load model from Hugging Face Hub
+model = load_model("wav2sleep")
+
+# Or load from local checkpoint
+model = load_model("/path/to/checkpoint")
+```
+
+For inference on new data:
+
+```python
+from wav2sleep import load_model, predict_on_folder
+
+model = load_model("wav2sleep")
+predict_on_folder(
+    input_folder="/path/to/edf_files",
+    output_folder="/path/to/predictions",
+    model=model,
+)
+```
+
+## Training Data
+
+The model was trained on polysomnography data from multiple publicly available datasets
+managed by the National Sleep Research Resource (NSRR), including SHHS and MESA.
+
+## Citation
+
+```bibtex
+@misc{carter2024wav2sleep,
+    title={wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals},
+    author={Jonathan F. Carter and Lionel Tarassenko},
+    year={2024},
+    eprint={2411.04644},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG},
+}
+```
+
+## License
+
+Apache 2.0