International Journal of Advanced Engineering Application

A Low-Cost Discrete Wavelet Transform Framework for ECG Signal Denoising on Resource-Constrained Embedded Platforms

Author(s):Pratiksha S. Gawali, Mohammed Irfan Shaikh, Vinay K. Bhure

Affiliation: Dept. of Electronics & Telecom Engg., Shri Tuljabhavani College of Engineering, Tuljapur, Maharashtra, India, Dept. of Biomedical Engg., Vidya Pratishthan's Institute of Technology, Indapur, Maharashtra, India, Dept. of Instrumentation Engg., Bapurao Deshmukh College of Engineering, Sevagram, Maharashtra, India

Page No: 1-5

Volume issue & Publishing Year: Volume 3, Issue 4, 2026/05/01

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

Download PDF Cite this article

Abstract:
Electrocardiogram (ECG) signals acquired in semi-rural primary health centers using low-cost AD8232 front-ends are routinely corrupted by power-line interference (50 Hz), baseline wander caused by electrode motion, and broadband electromyographic noise. This paper presents a lightweight Discrete Wavelet Transform (DWT) denoising pipeline benchmarked on 30 records of the MIT-BIH Arrhythmia Database (fs = 360 Hz). Seven wavelet families are compared at decomposition levels L = 2–7 using Donoho's universal soft-threshold rule with a robust median absolute deviation estimator. The proposed db6 wavelet at L = 5 yields an output SNR of 7.46 dB from a 1.17 dB input — an improvement of 6.29 dB — while preserving the QRS morphology with only 0.0019 mean-square error. The algorithm has a complexity of O(N) and executes in 11.3 ms on an ESP32 (240 MHz), making it deployable on rural tele-cardiology kiosks costing under ₹2,500.

Keywords: ECG denoising; Discrete wavelet transform; Daubechies wavelet; Universal threshold; MIT-BIH; Embedded ESP32

Reference:

• [1] Donoho, D.L. (1995). De-noising by soft-thresholding. IEEE Trans. Information Theory, 41(3), 613–627.
• [2] Mallat, S. (2009). A Wavelet Tour of Signal Processing, 3rd ed. Academic Press.
• [3] Moody, G.B., Mark, R.G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Eng. Med. Biol. Mag., 20(3), 45–50.
• [4] Singh, B.N., Tiwari, A.K. (2006). Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing, 16(3), 275–287.
• [5] Alfaouri, M., Daqrouq, K. (2008). ECG signal denoising by wavelet transform thresholding. American Journal of Applied Sciences, 5(3), 276–281.
• [6] Poornachandra, S. (2008). Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Processing, 18(1), 49–55.
• [7] Awal, M.A. et al. (2014). An adaptive level dependent wavelet thresholding for ECG denoising. Biocybernetics & Biomedical Eng., 34(4), 238–249.
• [8] Kumar, R., Saini, I. (2019). Empirical wavelet transform based ECG signal denoising. Biomed. Signal Processing & Control, 49, 122–129.