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https://github.com/willxxy/text-egm
[CHIL 2024] Interpretation of Intracardiac Electrograms Through Textual Representations
https://github.com/willxxy/text-egm
cardiology deep-learning electrophysiology healthcare interpretability language-model machine-learning masked-language-models physiological-signals physiology representation-learning
Last synced: about 23 hours ago
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[CHIL 2024] Interpretation of Intracardiac Electrograms Through Textual Representations
- Host: GitHub
- URL: https://github.com/willxxy/text-egm
- Owner: willxxy
- License: cc0-1.0
- Created: 2024-04-05T17:28:50.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-09-04T16:08:25.000Z (4 months ago)
- Last Synced: 2024-09-05T22:32:49.636Z (4 months ago)
- Topics: cardiology, deep-learning, electrophysiology, healthcare, interpretability, language-model, machine-learning, masked-language-models, physiological-signals, physiology, representation-learning
- Language: Python
- Homepage:
- Size: 50.8 KB
- Stars: 8
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Interpretation of Intracardiac Electrograms Through Textual Representations
William Jongwon Han, Diana Gomez, Avi Alok, Chaojing Duan, Michael A. Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao.
Official code for "[Interpretation of Intracardiac Electrograms Through Textual Representations](https://arxiv.org/abs/2402.01115)" accepted by 2024 Conference on Health, Inference, and Learning (CHIL).
If you experience any bugs or have any questions, please submit an issue or contact at wjhan{at}andrew{dot}cmu{dot}edu.
We thank the Mario Lemieux Center for Heart Rhythm Care at Allegheny General Hospital for supporting this work.
## Set Up Environment
Note: We have only tested on Ubuntu 20.04.5 LTS.
1. `conda create -n envname python=3.8`
2. `conda activate envname`
3. `git clone https://github.com/willxxy/ekg-af.git`
4. `cd ekg-af`
5. `pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113`
Note: Please ensure that the pip you are using is from the conda environment
6. Test if pytorch version is compatible with current, available gpus by executing `python gpu.py`. Currently, we have only tested on A5000 (24 GB) and A6000 (48 GB) NVIDIA GPUs.
7. `pip install -r requirements.txt`
## Set Up Data
1. Although the data we curated is not publicly available, we do have experimental results on an external dataset (main results are in Table 2 in the paper), namely the "Intracardiac Atrial Fibrillation Database" available on PhysioNet.
2. To set up this data, `cd` into the `preprocess` folder.
3. Please execute the following command to download the data.
```
wget https://physionet.org/static/published-projects/iafdb/intracardiac-atrial-fibrillation-database-1.0.0.zip
```
4. Unzip the file by executing
```
unzip intracardiac-atrial-fibrillation-database-1.0.0
```
5. Now execute the folllowing command to preprocess the data.
```
sh preprocess.sh
```
6. This should create a data folder with several `.npy` for training, validation, and test.
## Start Training
1. From the preprocess folder `cd ../` back to the main directory.
2. You can now directly use `train.sh` files to start training.
## Inference
1. Please execute `sh inference.sh` after training. Make sure to specify the checkpoint path.
## Visualizations
All visualizations will be saved under their respective checkpoint folder.
Please `cd visualize` before visualizing.
Under the `visualize` folder, please view the following scripts:
1. `stitch.sh` - Visualizes the reconstructed and forecasted signals.
2. `viz_tokens.sh` - Visualizes the tokenized representation of the signal.
3. `viz_attentions.sh` - Visualizes the attention map of the model.
4. `viz_int_grad.sh` - Visualizes the attribution scores of the model.
## Citation
If you found this repository or work helpful to your own, please cite the following bibtex.
```
@misc{han2024interpretation,
title={Interpretation of Intracardiac Electrograms Through Textual Representations},
author={William Jongwon Han and Diana Gomez and Avi Alok and Chaojing Duan and Michael A. Rosenberg and Douglas Weber and Emerson Liu and Ding Zhao},
year={2024},
eprint={2402.01115},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```