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At present many of the ECG recording instruments are based on analog recording circuits. Because of this, noise from various sources is inherently added to the signal. Sometimes the noise power becomes even larger than the signal. This study identifies various sources of noise that usually corrupt the ECG signal and attempts to get rid of such noises. Various filtration techniques are used such as low pass filter, high pass filter, band pass filter and notch filter to filter the noise signal. Also implemented is a more filter called the mobile averaging filter which has shown very good efficiency by smoothing the waveform and suppressing the noise of the 50 Hz power line.
New improved methods for the elimination of the electrocardiogram (ECG) signal based on the adaptive filter with empirical mode decomposition (EMD) and Empirical Mode Set (EME) decomposition are proposed. EMD and EEMD methods are used to decompose the ECG signal into intrinsic mode functions (IMF). Improved performance of traditional EMD-based waste disposal methods through the adaptive processing of IMF components that are related to ECG noise. Convergence problem in the least squares (LMS) algorithm driven by the adaptive algorithm based on EEMD. Block the least square mean (ELMS) algorithm used with EMD and EEMD to improve the computational efficiency of adaptive processing. The proposed methods are applied in white Gaussian noise ECG signal and real-time ECG signals obtained from the arrhythmia database of the MIT-BIH physiotherapist. The signal-to-noise ratio (SNR), the correlation coefficient and the mean squared error (MSE) are used to measure and compare the performance of the proposed methods with traditional EMD-based methods. All experiments performed with MATLAB-based coding. The results show that EEMD with ELMS algorithm has better results than traditional methods based on EMD.
