ECG SIGNAL DENOISING USING DISCRETE WAVELET TRANSFORM

MOHAMMED ABDALLAH  ALI; SERWAN  ALI; AHMED  KHORSHEED

doi:10.26682/csjuod.2023.26.2.42

MOHAMMED ABDALLAH ALI College of Engineering, University of Duhok, Kurdistan Region-Iraq
SERWAN ALI College of Engineering, University of Duhok, Kurdistan Region-Iraq
AHMED KHORSHEED College of Engineering, University of Duhok, Kurdistan Region-Iraq

DOI: https://doi.org/10.26682/csjuod.2023.26.2.42

Keywords: Denoising; electrocardiogram; Packet Wavelet Transform; Thresholding;

Abstract

The electrocardiogram (ECG) is crucial and widely used diagnostic tool for heart diseases; however, the presence of various noise components can distort the ECG waveform, leading to inaccurate interpretations. This research focuses on the utilization of Discrete Wavelet Transform (DWT) for denoising ECG signals. In this paper, we introduce an innovative DWT-based architecture for ECG denoising, designed to effectively eliminate reference line ᴡander, power line interference, white noise, muscle artifacts, and impulsive noise through a single comprehensive process that incorporates signal decomposition and thresholding. Experimental assessments were performed on the MIT-BIH database, utilizing a 360 Hz sampling frequency over a 15-second duration. Denoising performance was calculated by measuring improvements in Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR) under various noise power levels. Our proposed DWT-based algorithm consistently outperformed traditional filter-based techniques, demonstrating superior MSE and SNR enhancements. Notably, the average enhancements in Signal-to-Noise Ratio (SNR) ranged up to 10% for baseline wander, a reduction of -15 dB for power-line interference, and an increase of 50% for white noise when compared to conventional low-pass filter, notch filter, and moving average filter approaches, respectively. These improvements were consistently experimental across different noise power levels, highlighting substantial gains in signal clarity. Additionally, significant reductions in MSE, with improvements of 0.003, further underscored the effectiveness of proposed architecture. These quantitative results affirm the accuracy and efficiency of our method, offering substantial enhancements in ECG signal quality and clarity. This improvement contributes to more precise subsequent analyses, ultimately benefiting the diagnosis and treatment of heart diseases.

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