NDLI: Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

Content Provider	IET Digital Library
Author	Bose, Rohit Pratiher, Sawon Chatterjee, Soumya
Abstract	Here, a technique for automated detection of epilepsy is proposed, based on a novel set of features derived from the multifractal spectrum of electroencephalogram (EEG) signals. In fractal geometry, multifractal detrended fluctuation analysis (MDFA) is a technique to examine the self-similarity of a non-linear, chaotic and noisy time series. EEG signals which are representatives of complex human brain dynamics can be effectively characterised by MDFA. Here, EEG signals representing healthy, interictal and seizure activities are acquired from an available dataset. The acquired signals are at first analysed using MDFA. Based on the multifractal analysis, 14 novel features are proposed in this study, to distinguish between different types of EEG signals. The statistical significance of the selected features is evaluated using Kruskal–Wallis test and is finally served as input feature vector to a support vector machines classifier for the classification of EEG signals. Four classification problems are presented in this work and it is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.
Starting Page	157
Ending Page	164
Page Count	8
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 2, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5258
Journal	IET Signal Processing
Publisher Date	2018-09-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated EEG Signal Detection Bioelectric Potentials Bioelectric Signal Biology And Medical Computing Chaos Chaotic Time Series Complex Human Brain Dynamics Computer-aided Diagnosis Digital Signal Processing Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Acquisition Electroencephalography Epileptic Seizure Activities Feature Extraction Fluctuations Fractals Interictal Seizure Activities Knowledge Engineering Technique Kruskal–Wallis Test Medical Disorders Medical Signal Detection Medical Signal Processing Multifractal Detrended Fluctuation Analysis Multifractal Spectrum Neurophysiology Noisy Time Series Nonlinear Time Series Self-similarity Signal Detection Support Vector Machine Support Vector Machine Classifier Time Series
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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