ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Compressive sensing of ECG signals using plug-and-play regularization

Unni, VS and Gavaskar, RG and Chaudhury, KN (2023) Compressive sensing of ECG signals using plug-and-play regularization. In: Signal Processing, 202 .

sig_pro_202_2023.pdf - Published Version

Download (1MB) | Preview
Official URL: https://doi.org/10.1016/j.sigpro.2022.108738


Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements remains a challenging problem. Traditional recovery methods are based on solving a regularized minimization problem, where a sparsity-promoting prior is used. In this paper, we propose an alternative iterative recovery algorithm based on the Plug-and-Play (PnP) method, which has recently become popular for imaging problems. In PnP, a powerful denoiser is used to implicitly perform regularization, instead of using hand-crafted regularizers; this has been found to be more successful than traditional methods. In this work, we use a PnP version of the Proximal Gradient Descent (PGD) algorithm for ECG recovery. To ensure mathematical convergence of the PnP algorithm, the signal denoiser in question needs to satisfy some technical conditions. We use a high-quality ECG signal denoiser fulfilling this condition by learning a Bayesian prior for small-sized signal patches. This guarantees that the proposed algorithm converges to a fixed point irrespective of the initialization. Importantly, through extensive experiments, we show that the reconstruction quality of the proposed method is superior to that of state-of-the-art methods. © 2022 Elsevier B.V.

Item Type: Journal Article
Publication: Signal Processing
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Biomedical signal processing; Electrocardiography; Gradient methods; Recovery; Signal reconstruction, Compressive sensing; ECG data compression; ECG signals; GMM denoiser; Gradient-descent; Plug-and-play; Plug-and-play regularization; Proximal gradient descent; Random vectors; Regularisation, Compressed sensing
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 27 Jan 2023 09:06
Last Modified: 27 Jan 2023 09:06
URI: https://eprints.iisc.ac.in/id/eprint/79540

Actions (login required)

View Item View Item