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      A review of epileptic seizure detection using machine learning classifiers

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          Abstract

          Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers—‘black-box’ and ‘non-black-box’. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.

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          The random subspace method for constructing decision forests

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            10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems.

            With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.
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              Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state

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                Author and article information

                Contributors
                khubeb@tec.mx
                rmm@tec.mx
                xhuang@csu.edu.au
                nmanzoor@ksu.edu.sa
                Journal
                Brain Inform
                Brain Inform
                Brain Informatics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2198-4018
                2198-4026
                25 May 2020
                25 May 2020
                December 2020
                : 7
                : 1
                Affiliations
                [1 ]GRID grid.419886.a, ISNI 0000 0001 2203 4701, School of Engineering and Sciences, Tecnologico de Monterrey, ; Av. E. Garza Sada 2501, Monterrey, Nuevo Leon Mexico
                [2 ]GRID grid.1037.5, ISNI 0000 0004 0368 0777, School of Computing and Mathematics, , Charles Sturt University, ; 2640 Albury, NSW Australia
                [3 ]GRID grid.56302.32, ISNI 0000 0004 1773 5396, College of Applied Studies and Community Service, , King Saud University, ; Riyadh, Kingdom of Saudi Arabia
                Article
                105
                10.1186/s40708-020-00105-1
                7248143
                32451639
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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