Denoising EOG Signal using Stationary Wavelet Transform

Open access

Denoising EOG Signal using Stationary Wavelet Transform

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.

Zhao Lv, Xiaoping Wu, Mi Li, Chao Zhang. (2008). Implementation of the EOG-based human computer interface system. In The 2nd International Conference on Bioinformatics and Biomedical Engineering (CBBE 2008), 16-18 May 2008, 2188-2191.

Reddy, M. S, Narasimha, B., Suresh, E., Rao, K. S. (2010). Analysis of EOG signals using wavelet transform for detecting eye blinks. In International Conference on Wireless Communications and Signal Processing (WCSP), 21-23 October 2010, IEEE, 1-4.

Bulling, A., Ward, J. A., Gellersen, H., Troster, G. (2011). Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (4), 741-753.

Mali, R. D., Khadtare, M. S., Bombale, U. L. (2011). Removal of 50Hz PLI using discrete wavelet transform for quality diagnosis of biomedical ECG signal. International Journal of Computer Applications, 23 (7), 1-6.

Krishnaveni, V., Jayaraman, S., Aravind, S., Hariharasudhan, V., Ramadoss, K. (2006). Automatic identification and removal of ocular artifacts from EEG using wavelet transform. Measurement Science Review, 6 (4), 45-57.

Kania, M., Fereniec, M., Maniewski, R. (2007). Wavelet denoising for multi-lead high resolution ECG signals. Measurement Science Review, 7 (4), 30-33.

Senthil Kumar, P., Arumuganathan, R., Sivakumar, K., Vimal, C. (2009). An adaptive method to remove ocular artifacts from EEG signals using wavelet transform. Journal of Applied Sciences Research, 5 (7), 741-745.

Raj, V. N. P., Venkateswarlu, T. (2011). ECG signal denoising using undecimated wavelet transform. In 3rd International Conference on Electronics Computer Technology (ICECT), 8-10 April 2011, IEEE, 94-98.

Mohan Kumar, B., Vidhya Lavanya, R. (2011). Signal denoising with soft threshold by using Chui-Lian (CL) multiwavelet. International Journal of Electronics & Communication Technology, 2 (1), 38-42.

Rosas-Orea, M. C. E., Hernandez-Diaz, M., Alarcon-Aquino, V., Guerrero-Ojeda, L. G. (2005). A comparative simulation study of wavelet based denoising algorithms. In 15th International Conference on Electronics, Communications and Computers (CONIELECOMP 2005), 28 February - 02 March, 2005, 125-130.

Núcleo de Engenharia Biomédica do Instituto Superior Técnico. (2009/10). Labs PDS20092010_materialExtra_eogSig.mat. http://nebm.ist.utl.pt/repositorio/ficheiros/1750

Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

Journal Information


IMPACT FACTOR 2017: 1.345
5-year IMPACT FACTOR: 1.253



CiteScore 2017: 1.61

SCImago Journal Rank (SJR) 2017: 0.441
Source Normalized Impact per Paper (SNIP) 2017: 0.936

Cited By

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 158 155 10
PDF Downloads 46 46 2