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Abstract: Electrocardiogram (ECG) signals are heavily influenced by a wide range of noise sources. The signal can be denoised by removing unwanted components from the representation. We present the new method, demonstrate how it works with signals, and discuss its properties. Power line interference (PLW), baseline noise, electrode motion artifact noise, and Electromyography (EMG) noise are the most commonly influenced noise on ECG signals. Denoising ECG signals is a critical step in obtaining pure signal features that can be extracted for accurate diagnosis. This research focuses on the various sources of common noise in ECG signals, as well as signal processing techniques for removing the noise. The discrete wavelet transform can be used to remove baseline noise from an ECG signal (DWT). Powerline noise can be removed it by Notch filter. Adaptive filtering is thought to be a good way to get rid of EMG noise, and we can solve the problem with a new algorithm. Least mean square (LMS) adaptive filters and recursive least square (RLS) filters are used to remove Electrode Motion artifact noise. The MIT-BIH arrhythmia dataset are utilizing for ECG analysis with Matlab2019b program.