ECGInstruct

Dataset for paper "Teach Multimodal LLMs to Comprehend Electrocardiographic Images".

🌐 Project Page: https://aimedlab.github.io/PULSE/

📄 Paper: https://arxiv.org/abs/2410.19008

🧑‍💻 Code: https://github.com/AIMedLab/PULSE

🤗 Model: https://huggingface.co/PULSE-ECG/PULSE-7B

⚖️ ECGBench: https://huggingface.co/datasets/PULSE-ECG/ECGBench

Introduction

ECGInstruct is a comprehensive and large-scale instruction-tuning dataset designed for ECG image interpretation. (1) The ECG images in this dataset are generated from raw signal recordings and include a range of distortions that simulate real-world printed ECG images. (2) ECGInstruct is carefully curated, drawing from clinician-defined ECG tasks, original diagnoses, clinical reports, and a variety of task types. To ensure high quality, additional checks are applied to filter out lower-scored instructions.

Dataset Statistics

Dataset Examples

Citation

If you find this work helpful, please cite our paper:

@article{liu2024teach,
  title={Teach Multimodal LLMs to Comprehend Electrocardiographic Images},
  author={Ruoqi Liu, Yuelin Bai, Xiang Yue, Ping Zhang},
  journal={arXiv preprint arXiv:2410.19008},
  year={2024}
}