A novel approach for noise removal and distinction of EEG recordings. (January 2018)

• Record Type: Journal Article
• Authors: Alharbi, Nader
• Is Part Of: Biomedical signal processing and control. Volume 39(2018)
• Page Start: 23
• Page End: 33
• Publication Date: 2018-01
• Subjects: EEG -- Noise removal -- Distinction -- Epileptic -- Eigenvalues; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.07.011
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A novel approach for filtering and distinguishing EEG recordings is proposed. The approach relies on the distribution of scaled Hankel matrix eigenvalues. The approach is capable of discriminating between normal and epileptic seizure EEG signals and removing noise. Abstract: This paper presents a novel approach for the analysis of electroencephalography (EEG) signals. It is based on the distribution of the eigenvalues of a scaled Hankel matrix, which can enable us to determine the number of eigenvalues required for noise removal and signal extraction in singular spectrum analysis. This paper examines the applicability of the approach to discriminate between epileptic seizure and normal EEG signals, the extraction of attractive patterns, the filtering of EEG signals and the elimination of the noise included in the signals. Various criteria are used as features to distinguish between epileptic and normal EEG segments. The results indicate the capability of the approach for noise removal in both EEG signals, and for discrimination between them.