Adaptive denoising of ecg signal using wavelet based wiener filter and lms filtering
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The proposed work mainly focuses on reduction of EMG (Electromyogram) noise in ECG signal. The use of Wavelet Transform (WT) can be effective for suppressing EMG (muscle) noise compared to linear filtering as it provides information about both time and frequency characteristics simultaneously. The proposed algorithm reduces EMG noise using wavelet wiener filtering. Parameters of wiener filter are adapted according to the level of interference in the input signal. Important parameters used for adaptation are decomposition depth of input signal, thresholding method used, threshold size and filter banks. LMS (Least Mean Square) filtering of adaptively denoised ECG signal is also done to improve filtering performance. Testing is performed by taking ECG signal from standard MIT/BIH arrhythmia database. The proposed AWWF (Adaptive Wavelet Wiener Filtering) algorithm along with post LMS filtering provides better results by increasing SNR (Signal-to-Noise Ratio) and reducing mean square error.