International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com KONA VENKATA GANESH 4 3 (May - June) 2022 05 May-June enECG Signal ProcessingNoise CancellationAdaptive FiltersLMS AlgorithmRLS Algorithm Electrocardiogram ECG signals are crucial for diagnosing cardiac abnormalities however they are often contaminated by various types of noise such as power line interference baseline wander and muscle artifacts These unwanted signals can obscure important cardiac features and lead to inaccurate diagnoses This study presents a simulationbased approach for noise cancellation in ECG signals using adaptive filtering techniques The proposed system employs algorithms such as the Least Mean Squares LMS and Recursive Least Squares RLS filters to effectively reduce noise while preserving essential signal characteristics MATLAB simulations are conducted to analyze the performance of each adaptive filter in terms of convergence rate mean square error MSE and signaltonoise ratio SNR improvement Experimental results demonstrate that adaptive filters provide superior performance compared to fixed filters due to their ability to adjust coefficients dynamically based on signal variations Among the tested methods the RLS algorithm exhibits faster convergence and higher accuracy making it suitable for realtime biomedical signal processing This research highlights the significance of adaptive filtering in improving ECG signal quality thereby enhancing the reliability of automated cardiac diagnosis systems Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2022 All Rights Reserved