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MIT-BIH Arrhythmia Database

The proposed framework aims to address the limitations of deep learning applications for ECG signal classification. Firstly, we proposed a CNN-based autoencoder in a federated architecture to denoise the raw ECG signal from patients. When trained on the baseline dataset, The proposed autoencoder provided an excellent reconstruction of the raw input signals and improved the overall performance when applied in federated settings.

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Cite this as

Ali Razaa, Kim Phuc Tran, Ludovic Koehla, Shujun Li (2024). Dataset: MIT-BIH Arrhythmia Database. https://doi.org/10.57702/ye06lfce

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Additional Info

Field Value
Created December 2, 2024
Last update December 2, 2024
Defined In https://doi.org/10.48550/arXiv.2404.15333
Citation
  • https://doi.org/10.48550/arXiv.2208.10463
  • https://doi.org/10.48550/arXiv.2106.12498
  • https://doi.org/10.1109/ECAI58194.2023.10193930
  • https://doi.org/10.48550/arXiv.2005.08689
  • https://doi.org/10.1109/TBCAS.2019.2953001
  • https://doi.org/10.1016/j.knosys.2021.107763
  • https://doi.org/10.48550/arXiv.2301.09496
  • https://doi.org/10.48550/arXiv.1812.07421
Author Ali Razaa
More Authors
Kim Phuc Tran
Ludovic Koehla
Shujun Li
Homepage https://physionet.org/content/miib/1.0/