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A Power-Efficient Approximate Multi-Level Discrete Haar Wavelet Transform Design for ECG Data Compression
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DOI：10.1007/s00034-026-03545-y.png)
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Abstract
En 中文
This work proposes a highly efficient compression scheme utilizing a VLSI-based discrete Haar Wavelet transform (DHWT) architecture. This scheme aims to facilitate improved transmission and storage, particularly in resource-constrained environments for electrocardiogram (ECG) signal processing. It presents pruned, approximate and pruned, and truncated DHWT architectures (PDHWT, AxPDHWT, and TDHWT, respectively) up to level 5, targeting ultra-high energy efficiency in ECG data compression. Among the developed solutions, the most energy-efficient and area-optimized approach simultaneously employs all three compression techniques (pruning, approximation, and truncation). The PDHWT technique significantly saves energy by eliminating redundant components in this combined approach. The AxPDHWT approach enhances performance by removing the most computationally expensive elements. Finally, TDHWT reduces the number of input bits, leading to a lower-complexity architecture. The combined techniques achieve a minimum compression ratio (MCR) of 0.03125 (132\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{32}$$\end{document}) and a percent root difference (PRD) of less than 2.08. The VLSI architecture implementation leverages 65 nm CMOS technology, with a maximum frequency of 1.10GHz, and a target frequency of 125KHz (for comparison with the results found in the literature). The most power-efficient solution combines all three techniques (truncation, pruning, and approximation), achieving significant power savings. Specifically, the architecture with truncation demonstrates exceptional performance, achieving a compression ratio of 116\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{16}$$\end{document}, a worst-case accuracy of 0.96, a PRD of 4.41, a total area of 771.20 mu m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu m<^>{2}$$\end{document}, and the lowest power consumption of 0.60 mu W\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu W$$\end{document}.
Keywords:
Data compression
Wavelet transform
VLSI
Electrocardiography
Energy efficiency
Journal
C
IF:
2
Papers:
266
Citations:
0
