Abstract: An electro cardio gram (ECG) signal is a series of waves and deflections which represents the electrical activity of the heart over time. ECG signal can be contaminated by different forms of noises. These noises mislead the ECG annotators from accurate identification of the ECG signal features. Conventional filters are used to reduce the noise components in ECG signal from different unwanted frequency components. But this filtering process is not efficiently work on the contaminated ECG signals. In biomedical signals reduction of noise is difficult with the fixed coefficients filters, because these signals are random in nature and not exact known depending on the time. To overcome this type of problem we require an adaptive algorithm technique. In this paper we present improved Kalman filter developed with a state space model and autocorrelation least squares(ALS) technique to estimate the state variables, including the Gaussian noise approximation from the previous values of the original ECG signal. This filter enhances the quality of the ECG signal and shows the good convergence properties. The results have been concluded with the MIT-BIH arrhythmia data base and MATLAB software.
Keywords: ECG signal, Gaussian noise, Adaptive algorithm, Kalman filter, SNR.
Title: Gaussian Noise Filtering From ECG Signal Using Improved Kalman Filter
Author: Venkata Rami Reddy .D, Abdul Rahim .B, Fahimuddin.Shaik
International Journal of Engineering Research and Reviews
ISSN 2348-697X (Online)
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