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Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition
Department of Control Systems and Signal Processing, School of Electrical Engineering, University of of Belgrade, Serbia, Department of Information Engineering, Computer Science and Mathematics, University of of lAquila, Italy.
Department of Control Systems and Signal Processing, School of Electrical Engineering, University of of Belgrade, Serbia.
Department of Information Engineering, Computer Science and Mathematics, University of of lAquila, Italy.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2014 (English)In: Biomedical Engineering: Applications, Basis and Communications, ISSN 1016-2372, Vol. 26, no 2, 1450021- p.Article in journal (Refereed) Published
Abstract [en]

The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pattern recognition. The decision making process is comprised of three main stages: (a) feature extraction based on wavelet transform, (b) feature space dimension reduction using scatter matrices and (c) classification by quadratic classifiers. The proposed methodology was applied on EEG data sets that belong to three subject groups: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval and (c) epileptic subjects during a seizure. An overall classification accuracy of 99% was achieved. The results confirmed that the proposed algorithm has a potential in the classification of EEG signals and detection of epileptic seizures, and could thus further improve the diagnosis of epilepsy.

Place, publisher, year, edition, pages
World Scientific, 2014. Vol. 26, no 2, 1450021- p.
Keyword [en]
Dimension reduction; Epilepsy diagnosis; Quadratic classifiers; Scatter matrices; Seizure detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-110540DOI: 10.4015/S1016237214500215Scopus ID: 2-s2.0-84896329075OAI: oai:DiVA.org:liu-110540DiVA: diva2:746664
Available from: 2014-09-14 Created: 2014-09-12 Last updated: 2014-09-18Bibliographically approved

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Gustafsson, Fredrik

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