Electrocardiogram Reconstruction Based on Compressed Sensing

@article{Zhang2019ElectrocardiogramRB,
  title={Electrocardiogram Reconstruction Based on Compressed Sensing},
  author={Zhimin Zhang and Karen Xinwen Liu and Shoushui Wei and Hongping Gan and Feifei Liu and Yuwen Li and C. Liu and Feng Liu},
  journal={IEEE Access},
  year={2019},
  volume={7},
  pages={37228-37237}
}
Compressed Sensing (CS) attempts to acquire and reconstruct a sparse signal from a sampling much below the Nyquist rate. In this paper, we proposed novel CS algorithms for reconstructing under-sampled and compressed electrocardiogram (ECG) signal. In the proposed CS-ECG scheme, the ECG signal was first sub-sampled randomly and mapped onto a two-dimensional (2D) space by using Cut and Align (CAB), for the purpose of promoting sparsity. A nonlinear optimization model was then used to reconstruct… Expand
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