Epilepsy and seizure characterisation by multifractal analysis of EEG subbands. (March 2018)

• Record Type: Journal Article
• Authors: Sikdar, Debdeep; Roy, Rinku; Mahadevappa, Manjunatha
• Is Part Of: Biomedical signal processing and control. Volume 41(2018)
• Page Start: 264
• Page End: 270
• Publication Date: 2018-03
• Subjects: EEG -- Epilepsy -- MFDFA -- EEG analysis -- Epilepsy detection -- Multifractal; 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.2017.12.006
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The significance of multi-fractal attributes of EEG and its sub-bands is explored. Normal, ictal, and interictal EEGs are classified based on multi-fractal parameters. Performance measures for multi-class SVM classification are reported. Abstract: Electroencephalography (EEG) is often used for detection of epilepsy and seizure. To capture chaotic nature and abrupt changes, considering the nonlinear as well as nonstationary behaviour of EEG, a novel nonlinear approach of MultiFractal Detrended Fluctuation Analysis (MFDFA) has been proposed in this paper to address the multifractal behaviour of healthy (Group B), interictal (Group D) and ictal (Group E) patterns. Following wavelet based decomposition of EEG into its frequency subbands, multifracatal formalism has been applied to extract four features, namely, spectrum width (Δ α ), spectrum peak ( α 0 ), spectrum skewness ( B ) and Hurst's exponent ( H ). The effectiveness of the parameters has been also tested through statistical significance across the subbands. It has been found that no parameters in alpha subband exhibit significant differences across all the Groups, whereas, all the parameters for band-limited EEG significantly distinguish the Groups. However, at least one Group was found to be significantly isolated from the parameters across all the subbands. Furthermore, support vector machine (SVM) has been trained to classify the Groups with the multifractal features for different EEG subbands. An accuracy of 99.6% has been observed for the band limited EEG.