Design and Analysis of an Efficient Stationary Wavelet-Transform for EOG Signal Denoising

An efficient wavelet-based architectures are designed and presented during this research paper which may be used to denoising of Electrooculography (EOG) signals. Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same level of frequency spectrum. Electrooculography (EOG) plays a crucial role within the event of ophthalmological diagnosis and human computer interface (HCI) applications. Firstly, EOG signals are obtained from physio net fantasia database. Reducing the noises involved within the EOG signals is difficult because of sharing of same bandwidth for both signal and noise. For this reduction designing the methods of wavelet architectures like db4 in MATLAB software for denoising, as well as Signal to noise (SNR) and Mean Square Error (MSE) that also display the qualities for assessment and also compare with existing proposed methods like db3, db5. After simulation in MATLAB adopt Intel Quartus Standard Edition for Digital Signal Processing (DSP), which is used for engineering modelling and production of the proposed denoising approach with hardware consumption results. For hardware consumption, de-soc cyclone V FPGA series (5CSEMA5F31C6) is used by implementation of designed proposed model.