Model Based ECG Denoising Using Discrete Bionic WaveletTransform

Abstract

Electrocardiogram (ECG) is a measurement of bio-electric potential produced by rhythmical cardiac activities, contraction and relaxation of the cardiac muscle produced at sinoatrial (SA) node. This electric potential associated with the cardiac cycle can be detected at the surface of the body, amplified and recorded as a time record of each cardiac cycle. Different cardiac function such as heart rate, abnormality of rhythm can easily be identified by ECG and it is a low cost tools in the medical diagnostic system. Therefore, ECG signal modeling and processing is one of the most significant topic in biomedical signal analysis. Most of the ECG models are complex and their computational time is high. In this research, a Gaussian wave-based model is proposed which can simulate ECG wave as well as its P, Q, R, S and T components individually. In addition, dynamically shifting baseline of the model reduces the preprocessing of ECG signal. The coefficient of the model is calculated by nonlinear least square technique using Gauss-Newton algorithm.The model fits well with real ECG by Normalized Root Mean Square error (NRMSE) of 0.0034 at the normal condition. Further analyses have been performed to evaluate the models ability of representing the different cardiac Dysrhythmias like atrial fibrillation,brachycardia and tachycardia successfully. For better model fitting denoised ECG plays a significant role. Bionic wavelet Transform (BWT) is based on auditory model but it is not efficient for ECG signal processing since ECG is generated from the heart. So for denoising ECG, a new adaptive wavelet transform is developed based on heart- arterial interaction model. Adaptability is adjusted instantaneous amplitude of the signal and its first-order difference. The automatically adjusted resolution is achieved by introducing the active control mechanism of the cardiac system into the wavelet transform. It is very hard to know what entropy function used in the bio-system. This is the problems of other transforms. But, the discrete BWT uses active control mechanism in the cardiac system to adjust the wavelet function rather than entropy function as criterion. Moreover, due to various oscillating behavior of different types of ECG signal constant Quality factor (Q) of wavelet is not as effective as variable Q. is changed with the instantaneous value of a signal and it will make BWT more adaptive compared to Tunable The variable Q Q wavelet transform (TQWT). As in variable Q-wavelet transform like DBWT which is he discrete version of BWT, is changed with the instantaneous value of a signal and its first order difference instead of Q-factor is tuned to a fixed value in TQWT. In addition, our proposed modified S-median thresholding technique has an adjustable factor and introduced in the system for better performance. In order to compare DBWT with other wavelet transform, experiments on traditional WT, multi- Q adaptive BWT, TQWT were conducted on both constructed signals and real ECG signals. The results show that novel DBWT performs better than these three wavelet transforms, and is appropriate for cardiac signal processing, especially over noisy environment.