Performance Evaluation of Wavelet and Adaptive Filters for ECG Denoising in the LabVIEW Environment

Electrocardiogram (ECG) signals are essential for diagnosing cardiovascular diseases, but they are frequently corrupted by baseline drift, muscular noise, and power-line interference, which hinder reliable feature detection. Conventional FIR, IIR, and notch filters are often insufficient because they distort waveform morphology or lack adaptability to different noise sources. In this work, ECG denoising was implemented in the LabVIEW environment by simulating noisy signals with the Biomedical Toolkit, applying wavelet filtering with several families, and adaptive filtering with LMS and RLS algorithms, followed by performance evaluation using signal-to-noise ratio (SNR). The results show that wavelet filters, particularly db5 and coif5, achieved the best performance with SNR around 14 dB, while LMS and RLS reached about 10-12 dB, confirming wavelet filtering as the most reliable approach under the tested conditions.