Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
- Paper (Waiting for accepted by a journal, which included detailed information)
- The application algorithm
- Pretrained weights
- The samples for testing
- Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
- Comprehensive Evaluations, including signal-level, feature-level, and diagnostic-level
It is the open-source code for MCMA, which could reconstruct 12-lead ECG with arbitrary single-lead ECG.
If you find it is useful, please cite Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG
@inproceedings{
chen2024multichannel,
title={Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead {ECG} from Arbitrary Single-Lead {ECG}},
author={Jiarong chen and Wanqing Wu and Shenda Hong},
booktitle={Artificial Intelligence and Data Science for Healthcare: Bridging Data-Centric AI and People-Centric Healthcare},
year={2024},
url={https://openreview.net/forum?id=lIX6BKDPJW}
}
or
@misc{chen2024multichannelmaskedautoencodercomprehensive,
title={Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG},
author={Jiarong Chen and Wanqing Wu and Tong Liu and Shenda Hong},
year={2024},
eprint={2407.11481},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.11481},
}
It includes demo.py, the trained model, and sample data.
conda env create -f environment.yml
Contacting me at [email protected]
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