<![CDATA[Electrocardiogram (ECG) signals play a vital role in cardiac diagnostics but are highly susceptible to interference from Baseline Wander (BW), Powerline Interference (PLI), Electrode Motion (EM), and Muscle Artifact (MA), which may degrade diagnostic accuracy and increase false alarm rates in healthcare monitoring systems. The Stationary Wavelet Transform (SWT) is known to be effective in ECG denoising; however, its performance is significantly influenced by the choice of thresholding function. This study aims to comprehensively evaluate five thresholding functions (Soft, Hard, Semi Soft, Garrotte, and Stein) when integrated with SWT across various noise types and levels. The performance is assessed using three primary metrics: Root Mean Square Error (RMSE), Percentage Root Difference (PRD), and Signal-to-Noise Ratio (SNR) Improvement. Experimental results indicate that the Stein method delivers the highest signal quality improvement with an average SNR gain of 5.4 dB, while the Semi Soft method achieves the lowest error rates, reducing RMSE by up to 28% under low to medium noise conditions. Under high noise levels, all methods show similar degradation in performance. This study addresses a gap in the literature by providing a novel comparative analysis of thresholding strategies for ECG denoising using SWT. The findings serve as a valuable guide for optimizing noise removal in real-time ECG monitoring systems]]>
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