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 simulation-based 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 signal-to-noise 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 real-time 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.
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