File size: 2,056 Bytes
ef16a45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ---
license: mit
tags:
- sleep-staging
- wav2sleep
- polysomnography
- time-series
- pytorch
library_name: wav2sleep
pipeline_tag: other
---
# wav2sleep-eog
EOG-based sleep staging (5-class: Wake, N1, N2, N3, REM)
## Model Description
This is a **wav2sleep** model for automatic sleep stage classification from electrooculography (EOG).
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** | EOG-L, EOG-R |
| **Output Classes** | 5 |
| **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("hf://joncarter/wav2sleep-eog")
# 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("hf://joncarter/wav2sleep-eog")
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
MIT
|