NDLI: Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals

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Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals

Content Provider	IET Digital Library
Author	Parvez, Mohammad Zavid Paul, Manoranjan
Abstract	Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain disorders like epileptic seizure. Feature extraction and classification of EEG signals is the crucial task to detect the stages of ictal and interictal signals for treatment and precaution of epileptic patients. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering the non-abruptness phenomena and inconsistency in different brain locations. In this study, the authors present a new approach for feature extraction and classification by exploiting temporal correlation within EEG signals for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods, they divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input into LS-SVM to classify them. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity and accuracy of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.
Starting Page	467
Ending Page	475
Page Count	9
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 6, Aug (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0288
Journal	IET Signal Processing
Publisher Date	2015-06-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioelectric Signal Biology And Medical Computing Brain Disease Diagnosis Correlation Method Digital Signal Processing EEG EEG Signal Classification Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalogram Signal Temporal Correlation Electroencephalography Epilepsy Interictal Period Epileptic Seizure Detection Feature Classification Feature Extraction Human Brain Activity Interpolation And Function Approximation Knowledge Engineering Technique Least Square Support Vector Machine Least Squares Approximation Linear Algebra Matrix Decomposition Medical Disorders Medical Signal Detection Mental Disorder Treatment Numerical Analysis Numerical Approximation And Analysis Patient Diagnosis Patient Treatment Probability Theory Signal Classification Signal Detection Signal Representation Statistical Analysis Statistical Feature Extraction Statistics Stochastic Linearised SCUC Support Vector Machine Two-dimensional Matrix
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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