Epilepsy Detection Using EEG with Different Time Frames

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This article analyzes and classifies EEG signals using wavelets decomposition and support vector machines (SVM).

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5 p.

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Liu, Jianguo 2016.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Arts and Sciences to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 21 times , with 10 in the last month . More information about this article can be viewed below.

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Description

This article analyzes and classifies EEG signals using wavelets decomposition and support vector machines (SVM).

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5 p.

Notes

Abstract: The electroencephalogram (EEG) signal is widely used in clinical to investigate brain disorders and plays an important role in
the diagnosis of epilepsy. We analyse and classify EEG signals using wavelets decomposition and support vector machines
(SVM). In particular, we break the EEG waves into different time frames. Numerical experiment on a standard test data set
demonstrates that the proposed algorithm can achieve high accuracy on the prediction of epilepsy even when short period of
time duration is used.

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  • Advances in Biomedical Engineering Research, 2018. Terre Haute, Indiana: Science and Engineering Publishing Company

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  • Publication Title: Advances in Biomedical Engineering Research
  • Volume: 4
  • Pages: 18-22
  • Peer Reviewed: Yes

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UNT Scholarly Works

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  • 2016

Added to The UNT Digital Library

  • May 16, 2018, 2:54 p.m.

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Liu, Jianguo. Epilepsy Detection Using EEG with Different Time Frames, article, 2016; Terre Haute, Indiana. (digital.library.unt.edu/ark:/67531/metadc1152240/: accessed October 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Arts and Sciences.