hjt15574089453/ECGInstruct

---
license: apache-2.0
configs:
- config_name: 'ECGInstruct'
data_files:
- split: train
path: ECGInstruct.json
---

# ECGInstruct

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

🌐 Project Page: [https://aimedlab.github.io/PULSE/](https://aimedlab.github.io/PULSE/)

📄 Paper: [https://arxiv.org/abs/2410.19008](https://arxiv.org/abs/2410.19008)

🧑‍💻 Code: [https://github.com/AIMedLab/PULSE](https://github.com/AIMedLab/PULSE)

🤗 Model: [https://huggingface.co/PULSE-ECG/PULSE-7B](https://huggingface.co/PULSE-ECG/PULSE-7B)

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

<!-- #### Basic Feature Recognition -->

<!--  -->

<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/_7BeRux-Ghz9Sna3jQ6Qd.png" alt="ECG Image" width="700"/>

<!-- #### Morphology and Pathological Condition Identification -->

<!--  -->

<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/3w50ApBhpu4v53YXi6EDu.png" alt="ECG Image" width="700"/>

<!--  -->

<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/BdYXt0rNDxMODjIHWdXAL.png" alt="ECG Image" width="700"/>

<!--  -->

<img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/BS00FLqC3vOdq5QRM7R-3.png" alt="ECG Image" width="700"/>

### 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}
}

```