Characterization of focal EEG signals: A review

Epilepsy is a common neurological condition that can occur in anyone at any age. Electroencephalogram (EEG) signals of non-focal (NF) and focal (F) types contain brain activity information that can be used to identify areas affected by seizures. Generally, F EEG signals are recorded from the epilept...

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Vydáno v:Future generation computer systems Ročník 91; s. 290 - 299
Hlavní autoři: Acharya, U. Rajendra, Hagiwara, Yuki, Deshpande, Sunny Nitin, Suren, S., Koh, Joel En Wei, Oh, Shu Lih, Arunkumar, N., Ciaccio, Edward J., Lim, Choo Min
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2019
Témata:
Computer-aided detection system
Electroencephalogram signals
Epilepsy
Focal
Non-focal
Non-focal
Electroencephalogram signals
Computer-aided detection system
Focal
Epilepsy
ISSN:0167-739X, 1872-7115
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Shrnutí:Epilepsy is a common neurological condition that can occur in anyone at any age. Electroencephalogram (EEG) signals of non-focal (NF) and focal (F) types contain brain activity information that can be used to identify areas affected by seizures. Generally, F EEG signals are recorded from the epileptic part of the brain, while NF EEG signals are recorded from brain regions unaffected by epilepsy. It is essential to correctly detect F EEG signals, when and where they occur, as focal epilepsy can be successfully treated by surgical means. However, all EEG signals are complex and require highly trained personnel for right interpretation. To overcome the associated challenges, in this study a computer-aided detection (CAD) system to aid in the detection of F EEG signals has been developed, and the performance of nonlinear features for differentiating F and NF EEG signals is compared. Moreover, it is noted that nonlinear features can effectively capture concealed patterns and rhythms contained in the EEG signals. Overall, it was found that the CAD system will be useful to clinicians in providing an accurate and objective paradigm for localization of the epileptogenic area. •Non-focal (NF) and focal (F) EEG signals are analyzed.•The CAD system to aid in the detection of F EEG signals are discussed.•Nonlinear features can effectively capture concealed patterns and rhythms contained in the EEG signals.•Performance of nonlinear features for differentiating F and NF EEG signals is compared.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2018.08.044