Efficient Use of Bi-orthogonal Wavelet Transform for Cardiac Signals

Abstract

n the detection of various cardiac abnormalities the ECG finds its importance. ECG signal de-noising process in an embedded platform is a challenge which has to deal with several issues. Noise reduction in low amplitude ECG signals by various de-noising techniques is an important task of biomedical science. ECG signals are very low frequency signals of about 0.5Hz-100Hz. There are various types of artifacts which get added in ECG signals and change the original signal features, therefore there is a need of removal of these artifacts from the original ECG signal. The noises that commonly disturb the basic electrocardiogram signal are power line interference (PLI), electrode contact noise, motion artifacts, electromyography (EMG) noise and instrumentation noise. These noises can be classified according to their frequency content. In this paper, the wavelet transform based approach for removing noise is used. In this paper, the discrete wavelet transform (DWT) at level 8 was applied to noisy ECG signals and decomposition of these ECG signals was performed. After removal of noise component using thresholding technique, decomposed signal is again reconstructed using Inverse discrete wavelet transform (IDWT). Here for de-noising the ECG signal, bi-orthogonal wavelet transform is used and the most efficient idea for noise removal process is concluded with this wavelet transform. The simulation has been done in MATLAB environment with the help of SIMULINK. The experiments are carried out on MIT-BIH database. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR), Peak Signal-to-noise ratio (PSNR) and visual inspection over the de-noised signal from each algorithm.