Title: Electroencephalogram signal quality enhancement by total variation denoising using non-convex regulariser

Authors: Padmesh Tripathi

Addresses: School of Engineering and Technology, Sharda University, 32–34, Knowledge Park-III, 201306, Greater Noida, India

Abstract: Medical practitioners have great interest in getting the denoised signal before analysing it. EEG is widely used in detecting several neurological diseases such as epilepsy, narcolepsy, dementia, sleep apnea syndrome, Alzheimer's, insomnia, parasomnia, Creutzfeldt-Jakob diseases (CJD) and schizophrenia, etc. In the process of EEG recordings, a lot of background noise and other kind of physiological artefacts are present, hence, data is contaminated. Therefore, to analyse EEG properly, it is necessary to denoise it first. Total variation denoising is expressed as an optimisation problem. Solution of this problem is obtained by using a non-convex penalty (regulariser) in the total variation denoising. In this article, non-convex penalty is used for denoising the EEG signal. The result has been compared with wavelet methods. Signal to noise ratio (SNR) and root mean square error have been computed to measure the performance of the method. It has been observed that the approach used here works well in denoising the EEG signal and hence enhancing its quality.

Keywords: electroencephalogram; EEG; wavelet; artefact; denoising; regulariser; convex optimisation; epilepsy; tumours; empirical mode decomposition; EMD; principal component analysis; PCA; total variation.

DOI: 10.1504/IJBET.2020.107709

International Journal of Biomedical Engineering and Technology, 2020 Vol.33 No.2, pp.134 - 145

Received: 17 Jan 2017
Accepted: 14 Sep 2017
Published online: 10 Jun 2020 *

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