[ICME 2025] ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis

This is a repository for reproducing the paper ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis [Paper]

Usage

Prepare Datasets

We use 5 public datasets in our model, they can be downloaded from:

• MIMIC-IV-ECG
• Champan-Shaoxing-Ningbo (CSD)
• Shandong Provincial Hospital (SPH)
• PTB-XL
• CPSC2018 (The training dataset can be downloaded here if not accessible)

We provided the preprocessing code of these datasets, including extracting waveform data, converting to WFDB format, etc. Take the SPH dataset as an example:

python ./data/preprocess/preprocess_sph.py --data-dir /path/to/sph

You can also copy the .csv files in data/ to your datasets folders. But you still need to convert the data format in SPH and CPSC2018 to WFDB format.

The translated version of PTB-XL dataset is got from Fairseq-signals.

The ECG-Instruct datasets of ECG-Chat are provided in llava/playground/data/. ecg_instruct_45k.json is the combination of diagnosis.json and conversation.json. We also shared our prompts to build this two datasets in llava/playground/data/prompts/.

Due to the large size, the files new_record_list.csv in MIMIC-IV-ECG and pretraining dataset pretrain_mimic.json in our project can be downloaded here.

Train the Models

To train and evaluate the ECG CoCa model, please use the scripts in open_clip/.

To pretrain and fine-tune the ECG-Chat model, please use the scripts in llava/.

The codes for report generation evaluation and RAG are coming soon.

The ECG data augmentation methods implementation comes from torch_ecg. We also used the CKEPE prompt proposed in MERL to evaluate the zero-shot classification ability of our model.

Checkpoints

You can download our trained checkpoints from the following links: ECG-CoCa, ECG-Chat

Citation

If you think that our work is useful to your research, please cite using this BibTeX:

@inproceedings{zhao2025ecgchat,
  author={Zhao, Yubao and Kang, Jiaju and Zhang, Tian and Han, Puyu and Chen, Tong},
  booktitle={2025 IEEE International Conference on Multimedia and Expo (ICME)}, 
  title={ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis}, 
  year={2025},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ICME59968.2025.11209476}}

If you have questions about this repo, please submit an issue or contact [email protected].

Acknowledgement

• OpenCLIP: the codebase we used to build our ECG CoCa model.
• LLaVA: we used the code and architecture of LLaVA to build our ECG-Chat model.