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      Various epileptic seizure detection techniques using biomedical signals: a review

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          Abstract

          Epilepsy is a chronic chaos of the central nervous system that influences individual’s daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain’s electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time–frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection.

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          Most cited references23

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          Wavelets and filter banks: theory and design

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            Automated EEG analysis of epilepsy: A review

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              Automated diagnosis of epileptic EEG using entropies

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                Author and article information

                Contributors
                Yash_99yash@yahoo.co.in
                Journal
                Brain Inform
                Brain Inform
                Brain Informatics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2198-4018
                2198-4026
                10 July 2018
                10 July 2018
                December 2018
                : 5
                : 2
                : 6
                Affiliations
                ISNI 0000 0001 2294 6276, GRID grid.5591.8, School of Informatics, , Eötvös Loránd University, ; Budapest, Hungary
                Article
                84
                10.1186/s40708-018-0084-z
                6170938
                29987692
                6d47452f-21b7-4218-bffc-cb3ca1d082d4
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 10 October 2017
                : 27 June 2018
                Funding
                Funded by: Stipendium Hungricum fellowship
                Award ID: Stipendium Hungricum fellowship
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2018

                fourier transform,wavelet,epilepsy,electroencephalogram (eeg),hilbert transform,empirical mode decomposition,rational function,particle swarm optimization (pso)

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