SleepXViT

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Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals

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# SleepXViT
- Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals

## Framework Overview
![Framework_Overview](https://github.com/user-attachments/assets/39b3f782-9358-4b6c-89c3-65187765a5c5)

## Dataset
- ### [KISS Dataset](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=210)
    Jaemin Jeong, Wonhyuck Yoon, Jeong-Gun Lee, Dongyoung Kim, Yunhee Woo, Dong-Kyu Kim, Hyun-Woo Shin, __Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification__, *Sleep*, 2023;, zsad242, https://doi.org/10.1093/sleep/zsad242 (Only Avaliable at Restricted Area)
    ![KISS Dataset](https://github.com/user-attachments/assets/9a64aca5-02fd-49a0-872a-4c8d7d58db15)

- ### [SHHS Dataset](https://www.sleepdata.org/datasets/shhs)
    Zhang GQ, Cui L, Mueller R, Tao S, Kim M, Rueschman M, Mariani S, Mobley D, Redline S. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018 Oct 1;25(10):1351-1358. doi: 10.1093/jamia/ocy064. PMID: 29860441; PMCID: PMC6188513.
  Quan SF, Howard BV, Iber C, Kiley JP, Nieto FJ, O'Connor GT, Rapoport DM, Redline S, Robbins J, Samet JM, Wahl PW. The Sleep Heart Health Study: design, rationale, and methods. Sleep. 1997 Dec;20(12):1077-85. PMID: 9493915.

## Model Architecture
### Baseline Model
- SleepTransformer: see [sleeptransformer/](https://github.com/sarahhyojin/SleepXViT/tree/main/sleeptransformer)

### Intra-epoch ViT
![7_a_intra_epoch](https://github.com/user-attachments/assets/52b4ef9e-ab66-4d82-a713-a014e23f5134)

### Inter-epoch ViT
![7_b_inter_epoch](https://github.com/user-attachments/assets/7fefc057-c0ca-4b3b-82de-f38a09af0d14)

## Usage
- Model weights & labels in [Google Drive](https://drive.google.com/drive/folders/1ILZYBwcuSdyNSSg4z9UZ2FciHRs5FwX7?usp=sharing)
1. Download dataset into /data folder
2. Convert raw signal images into psg images (see [/data](https://github.com/sarahhyojin/SleepXViT/tree/main/data))
3. Train & Test Intra-epoch ViT
```
$ python3 intra_train.py
$ python3 intra_test.py
```
4. Extract feature vectors from Intra-epoch ViT
```
$ python3 extract_vectors.py
$ python3 inter_epoch_prepare.py
```
5. Train & Test Inter-epoch ViT
```
$ python3 inter_train.py
$ python3 inter_test.py
```

## Interpretability
### Intra-Epoch Interpretability
- Adopted Transformer Interpretability Beyond Attention Visualization (CVPR 2021) [[Github]](https://github.com/hila-chefer/Transformer-Explainability)
- [Notebook](https://github.com/sarahhyojin/SleepXViT/blob/main/interpretability/Inter_epoch_ViT_interpretability.ipynb)
![Picture2](https://github.com/user-attachments/assets/1f9a1a79-540a-4067-b994-c1082c0ad239)

### Inter-Epoch Interpretability
- Adopted same method as above.
- Using sliding window scheme and aggregated seq=10 softmax values to predict one Epoch.
- [Notebook](https://github.com/sarahhyojin/SleepXViT/blob/main/interpretability/Intra_epoch_ViT_interpretability.ipynb)
![InterEpoch](https://github.com/user-attachments/assets/b30fb910-2a6e-43dc-8f43-67644ded9406)

## Requirements
Python 3.8 and above.
```
numpy==1.24.3
pandas==2.0.3
torch==2.1.2
tqdm==4.65.0
opencv-python-headless==4.8.1.78
matplotlib==3.7.2
scikit-learn==1.3.2
timm==0.9.12
```

To install the requirements, use:
```
$ pip3 install -r requirements.txt
```

SleepXViT

SleepXViT Latest Version Download

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