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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
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Issues per year: 4
Current issue: May 2024
Next issue: Aug 2024
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

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


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Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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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: 864 | Views: 898

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%.


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Cited-By CrossRef

[1] Detection Method of Epileptic Seizures Using a Neural Network Model Based on Multimodal Dual-Stream Networks, Wang, Baiyang, Xu, Yidong, Peng, Siyu, Wang, Hongjun, Li, Fang, Sensors, ISSN 1424-8220, Issue 11, Volume 24, 2024.
Digital Object Identifier: 10.3390/s24113360
[CrossRef]

[2] Distance Optimization KNN and EMD based Lightweight Hardware IP Core Design for EEG Epilepsy Detection, Chen, Xuanxu, Zhang, Yuejun, Ai, Guangpeng, Wang, Lixun, Zhang, Huihong, Li, Xiangyu, Wang, Pengjun, Microelectronics Journal, ISSN 1879-2391, 2024.
Digital Object Identifier: 10.1016/j.mejo.2024.106335
[CrossRef]

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