Compressed sensing for ECG signal compression using DWT based sensing matrices

Abstract

ABSTRACTIn this article, we have investigated the 1-D discrete wavelet transform (DWT)-based measurement matrices for electrocardiogram (ECG) compression. Moreover, the current work examines the suitability of the diverse DWT matrices, namely Symlets, Battle, Coiflets, Vaidyanathan, and Beylkin wavelet families, for ECG compression. Furthermore, this article shows the comparative performance study of the proposed DWT matrices with conventional deterministic and random measurement matrices. Overall, the Battle1 wavelet-based measurement matrices demonstrate the enhanced performance against the db3, coif5, and sym6 based measurement matrices in terms of Percentage Root-Mean Squared Difference (PRD), Root Mean Square Error (RMSE), and Signal-to-Noise Ratio (SNR). Finally, it was seen that the proposed Battle1 matrix demonstrates the improved performance against the conventional measurement matrices such as the Karhunen–Loeve transform (KLT), Discrete Cosine Transform (DCT) matrix, and random Hadamard measurement matrix. Thus, the result shows the adequacy of DWT measurement matrices for the compression of ECG.KEYWORDS: ECG compressionCompressed sensing (CS)Wavelet transform Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics Approval and Consent to ParticipateThe authors declare that they have no human participants, their data or biological material used in this work.Consent for PublicationInformed consent was obtained from all authors included in the study.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2023.2258643Additional informationFundingThe author(s) reported that there is no funding associated with the work featured in this article.