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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| Veröffentlicht in: | Future generation computer systems Jg. 91; S. 290 - 299 |
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| Hauptverfasser: | , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.02.2019
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Hagiwara, Yuki Oh, Shu Lih Suren, S. Ciaccio, Edward J. Lim, Choo Min Koh, Joel En Wei Acharya, U. Rajendra Deshpande, Sunny Nitin Arunkumar, N. |
| Author_xml | – sequence: 1 givenname: U. Rajendra surname: Acharya fullname: Acharya, U. Rajendra email: aru@np.edu.sg organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 2 givenname: Yuki surname: Hagiwara fullname: Hagiwara, Yuki organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 3 givenname: Sunny Nitin surname: Deshpande fullname: Deshpande, Sunny Nitin organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 4 givenname: S. surname: Suren fullname: Suren, S. organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 5 givenname: Joel En Wei surname: Koh fullname: Koh, Joel En Wei organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 6 givenname: Shu Lih surname: Oh fullname: Oh, Shu Lih organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore – sequence: 7 givenname: N. orcidid: 0000-0001-9719-4451 surname: Arunkumar fullname: Arunkumar, N. organization: Department of Electronics and Instrumentation, SASTRA University, Thanjavur, India – sequence: 8 givenname: Edward J. surname: Ciaccio fullname: Ciaccio, Edward J. organization: Department of Medicine, Columbia University, NY, USA – sequence: 9 givenname: Choo Min surname: Lim fullname: Lim, Choo Min organization: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore |
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