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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2023 - 6

Epilepsy Seizure Prediction from EEG Signal Using Machine Learning Techniques

SIDAOUI, B. See more information about SIDAOUI, B. on SCOPUS See more information about SIDAOUI, B. on IEEExplore See more information about SIDAOUI, B. on Web of Science, SADOUNI, K. See more information about SADOUNI, K. on SCOPUS See more information about SADOUNI, K. on SCOPUS See more information about SADOUNI, K. on Web of Science
 
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Download PDF pdficon (1,354 KB) | Citation | Downloads: 776 | Views: 782

Author keywords
epilepsy seizure, EEG, prediction, Convolutional Neural Network, SVM

References keywords
detection(13), seizure(10), neural(9), learning(9), epilepsy(7), epileptic(6), deep(6), vector(5), support(5), networks(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-05-31
Volume 23, Issue 2, Year 2023, On page(s): 47 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.02006
Web of Science Accession Number: 001009953400006
SCOPUS ID: 85164319612

Abstract
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Automatic seizure prediction is an important task to help epilepsy patients and epilepsy specialists. In addition, measuring electrical activity in different brain parts is an important step before any prediction. The best tool for recording electrical activity is electroencephalography (EEG), which uses electrodes placed on the head. This paper examines the performance of the convolutional neural network (CNN) architectures and support vector machine (SVM) method for predicting epileptic seizure activity using rich information recorded in the signal of EEG segments. The proposed approach is based on 22 features extracted from different EEG segments to produce a representative dataset. SVM classification models and two CNN architectures are proposed to predict ongoing seizures and different states of epilepsy patients. Two CNN architectures are presented: the first is trained with a dataset of features extracted from the EEG signal, and the second is trained with a dataset of Scalogram images from the EEG signal, whose purpose is to predict the imminence of an epileptic seizure in patients. A dataset of 6 patients is used to predict all states of epilepsy patients. Both CNN architectures and binary SVM classifiers achieve a classification rate above 98%.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 8,770 TCR
SCOPUS® Citations for all references: 11,686 TCR

Web of Science® Average Citations per reference: 292 ACR
SCOPUS® Average Citations per reference: 390 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-26 00:31 in 174 seconds.




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