A Segmental Approach with SWT Technique for Denoising the EOG Signal

Naga Rajesh

doi:10.1155/2015/612843

Abstract

The Electrooculogram (EOG) signal is often contaminated with artifacts and power-line while recording. It is very much essential to denoise the EOG signal for quality diagnosis. The present study deals with denoising of noisy EOG signals using Stationary Wavelet Transformation (SWT) technique by two different approaches, namely, increasing segments of the EOG signal and different equal segments of the EOG signal. For performing the segmental denoising analysis, an EOG signal is simulated and added with controlled noise powers of 5 dB, 10 dB, 15 dB, 20 dB, and 25 dB so as to obtain five different noisy EOG signals. The results obtained after denoising them are extremely encouraging. Root Mean Square Error (RMSE) values between reference EOG signal and EOG signals with noise powers of 5 dB, 10 dB, and 15 dB are very less when compared with 20 dB and 25 dB noise powers. The findings suggest that the SWT technique can be used to denoise the noisy EOG signal with optimum noise powers ranging from 5 dB to 15 dB. This technique might be useful in quality diagnosis of various neurological or eye disorders.

Highlights

The electric field around the eye changes when it moves, producing an electrical signal known as Electrooculogram (EOG) signal
Let us consider the denoising analysis for the increasing segments of the five noisy EOG signals EOG5 dB, EOG10 dB, EOG15 dB, EOG20 dB, and EOG25 dB. By denoising these noisy EOG signals with Stationary Wavelet Transformation (SWT) technique, the maximum Root Mean Square Error (RMSE) values obtained are between 8.38 μV and 10.92 μV
The minimum RMSE values 4.59 μV and 7.81 μV obtained by denoising EOG20 dB and EOG25 dB signals are comparatively higher values over the minimum RMSE values obtained by denoising EOG5 dB, EOG10 dB, and EOG15 dB signals

Summary

Introduction

The electric field around the eye changes when it moves, producing an electrical signal known as Electrooculogram (EOG) signal. This is due to the formation of electric dipole, as the cornea and retina of the eye behave as positive and negative poles. To remove signal components from unwanted frequency ranges different types of digital filters are used. It is difficult to reduce artifacts caused by unexpected human behavior depending on the time, with fixed coefficients of the digital filters [2]. This problem can be overcome by adaptive filtering technique. A wavelet is a small wave which is oscillatory to discriminate between different frequencies and contains both the analyzed shape and the window [5]

Methods

Results

Conclusion