| --- |
| 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". |
|
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| 🌐 Project Page: [https://aimedlab.github.io/PULSE/](https://aimedlab.github.io/PULSE/) |
|
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| 📄 Paper: [https://arxiv.org/abs/2410.19008](https://arxiv.org/abs/2410.19008) |
|
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| 🧑💻 Code: [https://github.com/AIMedLab/PULSE](https://github.com/AIMedLab/PULSE) |
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| 🤗 Model: [https://huggingface.co/PULSE-ECG/PULSE-7B](https://huggingface.co/PULSE-ECG/PULSE-7B) |
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| ⚖️ ECGBench: [https://huggingface.co/datasets/PULSE-ECG/ECGBench](https://huggingface.co/datasets/PULSE-ECG/ECGBench) |
|
|
| ### Introduction |
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|
| **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. |
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|  |
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| ### Dataset Statistics |
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|  |
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| ### Dataset Examples |
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| <!-- #### Basic Feature Recognition --> |
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| <!--  --> |
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| <img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/_7BeRux-Ghz9Sna3jQ6Qd.png" alt="ECG Image" width="700"/> |
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| <!-- #### Morphology and Pathological Condition Identification --> |
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| <!--  --> |
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| <img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/3w50ApBhpu4v53YXi6EDu.png" alt="ECG Image" width="700"/> |
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| <!--  --> |
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| <img src="https://cdn-uploads.huggingface.co/production/uploads/640701cb4dc5f2846c91d4eb/BdYXt0rNDxMODjIHWdXAL.png" alt="ECG Image" width="700"/> |
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| <!--  --> |
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| <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} |
| } |
| |
| ``` |
|
|
