Using invariant translation to denoise electroencephalogram signals

Walters-Williams, Janett and Li, Yan (2011) Using invariant translation to denoise electroencephalogram signals. American Journal of Applied Sciences, 8 (11). pp. 1122-1130. ISSN 1546-9239

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Abstract

Problem statement: Because of the distance between the skull and the brain and their different resistivity’s, Electroencephalogram (EEG) recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdiagnosis, as such it is necessary to remove them before further analysis and processing can be done. Denoising is often done with Independent Component Analysis algorithms but of late Wavelet Transform has been utilized. Approach: In this study we utilized one of the newer Wavelet Transform methods, Translation-Invariant, to deny EEG signals. Different EEG signals were used to verify the method using the MATLAB software. Results were then compared with those of renowned ICA algorithms Fast ICA and Radical and evaluated using the performance measures Mean Square Error (MSE), Percentage Root Mean Square Difference (PRD) and Signal to Noise Ratio (SNR). Results: Experiments revealed that Translation-Invariant Wavelet Transform had the smallest MSE and PRD while having the largest SNR. Conclusion/Recommendations: This indicated that it performed superior to the ICA algorithms producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited with blanket permission of publisher. All of our scientific journals are open access scholarly journals that are available online to the readers without financial, legal, or technical barriers based on the theory to keep an article's content intact. Creative Commons licenses can be used to specify usage rights.
Depositing User: Dr Yan Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 14 Nov 2011 11:51
Last Modified: 03 Jul 2013 00:52
Uncontrolled Keywords: translation invariant wavelet transform; independent component analysis; electroencephalogram; mean square error (MSE); second order statistics (SOS); signal to noise ratio (SNR); discrete wavelet transform (DWT)
Fields of Research (FOR2008): 01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
11 Medical and Health Sciences > 1103 Clinical Sciences > 110320 Radiology and Organ Imaging
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970110 Expanding Knowledge in Technology
Identification Number or DOI: doi: 10.3844/ajassp.2011.1122.1130
URI: http://eprints.usq.edu.au/id/eprint/19990

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