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A Proximity Operator-Based Method for Denoising Biomedical Measurements

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Abstract

The reconstruction of biomedical signals from noisy measurements has been an indispensable research topic. A majority of biosignals exhibit typical piecewise characteristics. The recovery of these piecewise biomedical signals embedded in noise through conventional nonlinear filtering schemes fails due to the lack of proper balance between strict sparsity and smoothness-inducing property of regularizers at large noise levels. This work proposes a nonlinear convex optimization-based filtering approach, which incorporates a Moreau envelope-based regularizer using the majorized version of the total variation function. The source signals are restored by exploiting their piecewise characteristics through a majorized cost function. The majorized functions provide some relaxation in solving non-convex functions. The relaxation of the stringent sparsifying penalty provides the balance between the smoothness property and the piecewise features of biosignals. The optimality criterion for the proposed method is analyzed in this work. Furthermore, we evaluate the new method using a standard IoT platform. The recovery performance of this method is found to be superior to various state-of-the-art techniques for piecewise synthetic and real-world physiological signals corrupted by additive noise.

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Data Availability Statement

The data that support the experimental evaluations in this study are taken from the MIT-BIH, MIMIC, NSTDB, and PhysioNet online database which are duly cited in this paper.

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Acknowledgements

The authors thank SAC, Indian Space Research Organization (ISRO), Department of Space, Govt. of India, for supporting this research work.

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  1. Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi, U.P, 221005, India

    Priya Ranjan Muduli & Vinit Kumar

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  1. Priya Ranjan Muduli
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Muduli, P.R., Kumar, V. A Proximity Operator-Based Method for Denoising Biomedical Measurements. Circuits Syst Signal Process 42, 6253–6277 (2023). https://doi.org/10.1007/s00034-023-02400-8

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