NDLI: Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals

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Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals

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Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals

Content Provider	IET Digital Library
Author	Chatterjee, Soumya Pratiher, Sawon Bose, Rohit
Abstract	In this contribution, a method to segregate electroencephalogram (EEG) signals into focal (F) and non-focal (NF) groups has been proposed, employing a novel multifractal detrended fluctuation analysis (MFDFA)-based feature sets. Manifestations in the fractal behaviour occurring due to the subtle morphological changes in F and NF EEG signals, can serve as an essential presurgical intervention for automated detection of structural epileptogenic area within the human brain. Considering the above-said fact, in the present approach, EEG signals acquired from a publicly available database, are analysed using multifractal parameters to investigate the complex, non-linear and stochastic fluctuations. Based on MFDFA of EEG signals, four statistically significant, new set of features have been extracted, which are eventually being used as inputs to a support vector machines and k-nearest-neighbour classifiers for the purpose of classification of EEG signals. It has been observed that the proposed MFDFA aided feature extraction method delivers quite commensurable and even better results in discriminating F and NF EEG signals, compared with the existing methods studied on the similar database.
Starting Page	1014
Ending Page	1021
Page Count	8
ISSN	17518822
Volume Number	11
e-ISSN	17518830
Issue Number	Issue 8, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/11/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2017.0117
Journal	IET Science, Measurement & Technology
Publisher Date	2017-07-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioelectric Signal Biology And Medical Computing Data Handling Technique Digital Signal Processing EEG Signal Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Feature Extraction Feature Extraction Technique Focal Electroencephalogram Signal Detection Human Brain K-nearest-neighbour Classifier Knowledge Engineering Technique Medical Signal Detection Multifractal Detrended Fluctuation Analysis Nonfocal Electroencephalogram Signal Signal Classification Signal Detection Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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