Denoising EOG Signal using Stationary Wavelet Transform

Abstract

Eye movements are critical signs of the neurological disorders and they can be acquired by EOG. The EOG signal is electrical signal generated due to eye ball movements and is contaminated with brain signals and power line while recording. As the EOG signal is a non-stationary signal, it can be denoised by wavelet transformation techniques. The present work covers denoising of noisy EOG signal using Stationary Wavelet Transform (SWT), which was done with all suitable wavelets that are morphologically similar to an EOG signal by applying both Soft and Hard Thresholding methods. An EOG signal was simulated and added with noise to obtain noisy EOG signal. The wavelet analysis of the simulated noisy EOG signal reveals that the Biorthogonal 3.3 wavelet is the best wavelet to denoise by using SWT technique, wherein the yield achieved was good with Signal to Noise Ratio of 36.5882 dB and minimum Mean Square Error of 0.383313 for quality diagnosis.