<![CDATA[Electrocardiogram (ECG) signals are often affected by various types of noise, such as electromagnetic interference, patient body movement, and disturbances from other medical devices, which can degrade the signal quality and hinder accurate detection of heart disorders. This study aims to compare the performance of various filters in reducing noise in ECG signals, focusing on the Highpass-Lowpass filter and the Least Mean Square (LMS) adaptive filter. The testing was conducted using metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), average amplitude, total error, and computation time. The experimental results show that the LMS filter provides the best results, with an MSE value of 0.0045, SNR of 21.5 dB, and total error of 4.78, indicating its ability to produce a cleaner signal compared to the Highpass-Lowpass filter. The LMS filter also demonstrates good computational efficiency, with a time of 0.102 seconds. With its ability to dynamically adjust filter parameters, the LMS filter proves effective in reducing both low and high-frequency noise in ECG signals. This study shows that the LMS filter can be effectively applied to process ECG signals contaminated by noise and contributes to improving the accuracy of heart disorder diagnosis]]>
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