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Compressive Sensing (CS) has recently attracted attention for ECG datacompression. In CS, an ECG signal is projected onto a small set of randomvectors. Recovering the original signal from such compressed measurementsremains a challenging problem. Traditional recovery methods are based onsolving a regularized minimization problem, where a sparsity-promoting prior isused. In this paper, we propose an alternative iterative recovery algorithmbased on the Plug-and-Play (PnP) method, which has recently become popular forimaging problems. In PnP, a powerful denoiser is used to implicitly performregularization, instead of using hand-crafted regularizers; this has been foundto be more successful than traditional methods. In this work, we use a PnPversion of the Proximal Gradient Descent (PGD) algorithm for ECG recovery. Toensure mathematical convergence of the PnP algorithm, the signal denoiser inquestion needs to satisfy some technical conditions. We use a high-quality ECGsignal denoiser fulfilling this condition by learning a Bayesian prior forsmall-sized signal patches. This guarantees that the proposed algorithmconverges to a fixed point irrespective of the initialization. Importantly,through extensive experiments, we show that the reconstruction quality of theproposed method is superior to that of state-of-the-art methods.

Compressive Sensing of ECG Signals using Plug-and-Play Regularization