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Biomedical signal (EEG/sEMG/ECG) completion/imputation using diffusion model. "A robust denoising diffusion framework for completing missing regions of multiple biomedical signals"

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DBSCF

Visitors This is the official source code repository of "A robust denoising diffusion framework for completing missing regions of multiple biomedical signals".

Dataset

  • ECG/EMG/EEG data have been already processed in

    Dataset/ECG/processed_data/

    Dataset/EMG/processed_data/

    Dataset/EEG/processed_data/

How to run this project

This project contains signal completion and downstream tasks validation:

Step 1: Completing the signals through the proposed methods

python main_signal.py

  • Train/test the model by modifying the 'state'

Step 2: Checking the classification results after completion

python main_classifier.py

  • Train/test the model by modifying the 'state'

If our work is helpful to you, please Star it and kindly Cite our paper as:

@article{wang2026robust,
  title={A robust denoising diffusion framework for completing missing regions of multiple biomedical signals},
  author={Wang, Shurun and Tang, Hao and Himeno, Ryutaro and Sol{\'e}-Casals, Jordi and Caiafa, Cesar F and Aoki, Shigeki and Sun, Zhe},
  journal={Biomedical Signal Processing and Control},
  volume={113},
  pages={108788},
  year={2026},
  publisher={Elsevier}}

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Biomedical signal (EEG/sEMG/ECG) completion/imputation using diffusion model. "A robust denoising diffusion framework for completing missing regions of multiple biomedical signals"

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