Literature DB >> 19963526

EEG seizure prediction: Measures and challenges.

A Aarabi1, R Fazel-Rezai, Y Aghakhani.   

Abstract

Different types of analyses of scalp and intracranial electroencephalography (EEG) recordings using linear and nonlinear time series analysis method have been done. They showed strong evidence of detectable changes in the EEG dynamics from minutes up to several hours in advance of seizure onset. The predictive performance of univariate and bivariate measures, comprising both linear and non-linear approaches have been carried in different studies Direct comparison among different measures and methods in seizure prediction is not possible, unless they are applied to the same dataset. In this review paper, we describe different seizure prediction measures briefly and discuss the existing challenges.

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Year:  2009        PMID: 19963526     DOI: 10.1109/IEMBS.2009.5332620

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

Authors:  Paul Fergus; David Hignett; Abir Hussain; Dhiya Al-Jumeily; Khaled Abdel-Aziz
Journal:  Biomed Res Int       Date:  2015-01-29       Impact factor: 3.411

2.  Multi-Channel Vision Transformer for Epileptic Seizure Prediction.

Authors:  Ramy Hussein; Soojin Lee; Rabab Ward
Journal:  Biomedicines       Date:  2022-06-29

3.  A fuzzy logic system for seizure onset detection in intracranial EEG.

Authors:  Ahmed Fazle Rabbi; Reza Fazel-Rezai
Journal:  Comput Intell Neurosci       Date:  2012-03-28

4.  A novel dynamic update framework for epileptic seizure prediction.

Authors:  Min Han; Sunan Ge; Minghui Wang; Xiaojun Hong; Jie Han
Journal:  Biomed Res Int       Date:  2014-06-22       Impact factor: 3.411

  4 in total

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