REMOVAL OF ELECTRODE MOTION ARTIFACT IN ECG SIGNALS USING WAVELET BASED THRESHOLD METHODS WITH GREY INCIDENCE DEGREE

Abstract

Cardiovascular diseases are one of the most frequent and dangerous problems in modern society in nowadays. Unfortunately electrocardiograms (ECG) signals, during their acquisition process, are affected by various types of noise and artifacts due to the movement, or breathing of the patient, electrode contact, power-line interferences, etc. The aim of this study was to develop an algorithm to remove electrode motion artifact in ECG signals. Donoho and Johnstone proposed Wavelet thresholding de-noising method based on discrete wavelet transform (DWT) is suitable for non-stationary signals. The wavelet transform coefficient is processed by using grey relation analysis of the grey theory, and a new wavelet threshold method namely wavelet threshold method with grey incidence degree (GID) (or the GID threshold method) based is introduced. It shows that the signal smoothness and similarity of the two signal criteria have been greatly improved by the GID threshold method compared with existing threshold methods. According to the characteristics of different ECG signals, GID threshold method gets better results than it can adaptively deal with noise separation and details remaining of the two opposing signal problems, so as to provide a better choice for wavelet threshold methods of signal processing. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR) and visual inspection over the denoised signal from each algorithm. The experimental result shows that GID hard shrinkage method with sub-band or level dependent thresholding gives the best denoising performance on ECG signal. The result shows that soft threshold not always gives better denoising performance; it depends on which wavelet thresholding algorithm was chosen.