Focal and non-focal epilepsy localization: a review

The Focal and non-Focal Epilepsy is seen to be a chronic neurological brain disorder, which has affected ≈60 million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. S...

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Autores Principales: Hussein, Ahmed, Gomes, Chandima, Habash, Qais, Santamaria-Granados, Luz, Ramirez-Gonzalez, Gustavo, Arunkumar, N., Alzubaidi, Abbas K., Mendoza Moreno, Juan Francisco
Formato: Desconocido (Unknown)
Publicado: 2019
Materias:
Acceso en línea:http://hdl.handle.net/11634/16979
id ir-11634-16979
recordtype dspace
institution Universidad Santo Tomas
collection DSpace
topic Focal Epilepsy
Non-Focal Epilepsy
Time and Frequency Domain Features
Nonlinear Features
Machine Learning Algorithms
EEG Signal Analysis
spellingShingle Focal Epilepsy
Non-Focal Epilepsy
Time and Frequency Domain Features
Nonlinear Features
Machine Learning Algorithms
EEG Signal Analysis
Hussein, Ahmed
Gomes, Chandima
Habash, Qais
Santamaria-Granados, Luz
Ramirez-Gonzalez, Gustavo
Arunkumar, N.
Alzubaidi, Abbas K.
Mendoza Moreno, Juan Francisco
Focal and non-focal epilepsy localization: a review
description The Focal and non-Focal Epilepsy is seen to be a chronic neurological brain disorder, which has affected ≈60 million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. Several EEGbased approaches have been proposed and developed to understand the underlying characteristics of the epileptic seizures. Despite the fact that the early results were positive, the proposed techniques cannot generate reproducible results and lack a statistical validation, which has led to doubts regarding the presence of the pre-ictal state. Various methodical and algorithmic studies have indicated that the transition to an ictal state is not a random process, and the build-up can lead to epileptic seizures. This study reviews many recently-proposed algorithms for detecting the focal epileptic seizures. Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. The existing algorithms were compared and the need for implementing a practical and reliable new algorithm is highlighted. The research regarding the epileptic seizure detection research is more focused on the development of precise and non-invasive techniques for rapid and reliable diagnosis. Lastly, the researchers noted that all the methods that were developed for epileptic seizure detection lacks standardisation, which hinders the homogeneous comparison of the detector performance.
format Desconocido (Unknown)
author Hussein, Ahmed
Gomes, Chandima
Habash, Qais
Santamaria-Granados, Luz
Ramirez-Gonzalez, Gustavo
Arunkumar, N.
Alzubaidi, Abbas K.
Mendoza Moreno, Juan Francisco
author_facet Hussein, Ahmed
Gomes, Chandima
Habash, Qais
Santamaria-Granados, Luz
Ramirez-Gonzalez, Gustavo
Arunkumar, N.
Alzubaidi, Abbas K.
Mendoza Moreno, Juan Francisco
author_sort Hussein, Ahmed
title Focal and non-focal epilepsy localization: a review
title_short Focal and non-focal epilepsy localization: a review
title_full Focal and non-focal epilepsy localization: a review
title_fullStr Focal and non-focal epilepsy localization: a review
title_full_unstemmed Focal and non-focal epilepsy localization: a review
title_sort focal and non-focal epilepsy localization: a review
publishDate 2019
url http://hdl.handle.net/11634/16979
_version_ 1712104066373386240
spelling ir-11634-169792020-04-15T10:02:48Z Focal and non-focal epilepsy localization: a review Hussein, Ahmed Gomes, Chandima Habash, Qais Santamaria-Granados, Luz Ramirez-Gonzalez, Gustavo Arunkumar, N. Alzubaidi, Abbas K. Mendoza Moreno, Juan Francisco Focal Epilepsy Non-Focal Epilepsy Time and Frequency Domain Features Nonlinear Features Machine Learning Algorithms EEG Signal Analysis The Focal and non-Focal Epilepsy is seen to be a chronic neurological brain disorder, which has affected ≈60 million people in the world. Hence, an early detection of the focal epileptic seizures can be carried out using the EEG signals, which act as a helpful tool for early diagnosis of epilepsy. Several EEGbased approaches have been proposed and developed to understand the underlying characteristics of the epileptic seizures. Despite the fact that the early results were positive, the proposed techniques cannot generate reproducible results and lack a statistical validation, which has led to doubts regarding the presence of the pre-ictal state. Various methodical and algorithmic studies have indicated that the transition to an ictal state is not a random process, and the build-up can lead to epileptic seizures. This study reviews many recently-proposed algorithms for detecting the focal epileptic seizures. Generally, the techniques developed for detecting the epileptic seizures were based on tensors, entropy, empirical mode decomposition, wavelet transform and dynamic analysis. The existing algorithms were compared and the need for implementing a practical and reliable new algorithm is highlighted. The research regarding the epileptic seizure detection research is more focused on the development of precise and non-invasive techniques for rapid and reliable diagnosis. Lastly, the researchers noted that all the methods that were developed for epileptic seizure detection lacks standardisation, which hinders the homogeneous comparison of the detector performance. http://unidadinvestigacion.usta.edu.co 2019-05-30T18:22:53Z 2019-05-30T18:22:53Z 2018-08-24 Generación de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicos Hussein, A., Gomes, C., Habash, Q., Santamaria-Granados, L., Mendoza-Moreno, J. F., Ramirez-Gonzalez, G., . . . Alzubaidi, A. K. (2018). Focal and non-focal epilepsy localization: A review doi:10.1109/ACCESS.2018.2867078 http://hdl.handle.net/11634/16979 https://doi.org/10.1109/ACCESS.2018.2867078 R. S. Fisher, W. v. E. Boas, W. Blume, C. Elger, P. Genton, P. Lee, et al., "Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)," Epilepsia, vol. 46, pp. 470-472, 2005. R. S. Fisher, C. Acevedo, A. 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Haque, "Epilepsy and seizure detection using statistical features in the complete ensemble empirical mode decomposition domain," in TENCON 2015-2015 IEEE Region 10 Conference, 2015, pp. 1-6. P. M. Shanir, K. A. Khan, Y. U. Khan, O. Farooq, and H. Adeli, "Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG," Clinical EEG and neuroscience, p. 1550059417744890, 2017. K. Fu, J. Qu, Y. Chai, and T. Zou, "Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals," Biomedical Signal Processing and Control, vol. 18, pp. 179-185, 2015. I. Mporas, V. Tsirka, E. Zacharaki, M. Koutroumanidis, and V. Megalooikonomou, "Evaluation of time and frequency domain features for seizure detection from combined EEG and ECG signals," in Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, 2014, p. 28. D. Hernández, L. Trujillo, E. Z-Flores, O. Villanueva, and O. Romo-Fewell, "Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features," in Computer Science and Engineering—Theory and Applications, ed: Springer, 2018, pp. 167-182. Atribución-NoComercial-CompartirIgual 2.5 Colombia http://creativecommons.org/licenses/by-nc-sa/2.5/co/ application/pdf application/pdf CRAI-USTA Bogotá
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