A robust methodology for classification of epileptic seizures in EEG signals

Author:

Tzimourta Katerina D.,Tzallas Alexandros T.,Giannakeas Nikolaos,Astrakas Loukas G.,Tsalikakis Dimitrios G.,Angelidis Pantelis,Tsipouras Markos G.

Publisher

Springer Science and Business Media LLC

Subject

Biomedical Engineering,Applied Microbiology and Biotechnology,Bioengineering,Biotechnology

Reference43 articles.

1. World Health Organization. Epilepsy. Fact sheet N999, [Online], 2012, Available: http://www.who.int/mediacentre/factsheets/fs999/en/ . Retrieved 15 Feb. 2018.

2. Sharma AK, Rani E, Waheed A, Rajput SK. Pharmacoresistant epilepsy: a current update on non-conventional pharmacological and non-pharmacological interventions. J Epilepsy Res. 2015;5(1):1–8.

3. Scheffer IE, Berkovic S, Capovilla G, Connolly MB, French J, Guilhoto L, et al. ILAE classification of the epilepsies: position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017;58(4):512–21.

4. Tzallas AT, Tsipouras MG, Tsalikakis DG, Karvounis EC, Astrakas L, Konitsiotis S, Tzaphlidou M. Automated epileptic seizure detection methods: a review study. In Epilepsy-histological, electroencephalographic and psychological aspects. 2012. www.intechopen.com

5. Sharma RR, Pachori RB. Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Meas Technol. 2018;12(1):72–82.

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