This repository contains the code for the paper "ECG-LLM: Leveraging Large Language Models for Low-Quality ECG Signal Restoration" published by IEEE in 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

https://ieeexplore.ieee.org/document/10822461.

1. Install Pytorch and necessary dependencies.

pip install -r requirements.txt

1. Put the datasets [Google Drive] under the folder ./dataset/.

2. Download the large language models from Hugging Face and specify the model path using the llm_ckp_dir parameter in scripts.

   • LLaMA2-7B
   • LLaMA3-8B

3. Train and evaluate the model. We provide all the above tasks under the folder ./scripts/.

# ecg forecasting
bash ./scripts/time_series_forecasting/long_term/AutoTimes_ETTh1.sh

It is recommended that your graphics card computing power is greater than or equal to an RTX 3090-24G.

We appreciate the following GitHub repos a lot for their valuable code and efforts.

• Time-Series-Library (https://github.com/thuml/Time-Series-Library)

If you have any questions or want to use the code, feel free to contact:

• Yong Liu (lf.liu@siat.ac.cn)