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Classification of E...
Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition
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- Gajic, D. (författare)
- 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
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- Djurovic, Z. (författare)
- Department of Control Systems and Signal Processing, School of Electrical Engineering, University of of Belgrade, Serbia
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- Di Gennaro, S. (författare)
- Department of Information Engineering, Computer Science and Mathematics, University of of lAquila, Italy
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- Gustafsson, Fredrik (författare)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
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(creator_code:org_t)
- World Scientific, 2014
- 2014
- Engelska.
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Ingår i: Biomedical Engineering: Applications, Basis and Communications. - : World Scientific. - 1016-2372 .- 1793-7132. ; 26:2, s. 1450021-
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.4...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Dimension reduction; Epilepsy diagnosis; Quadratic classifiers; Scatter matrices; Seizure detection
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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