Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

被引：0
|
作者：
Emara H.M. [1 ]
Elwekeil M. [1 ]
Taha T.E. [1 ]
El-Fishawy A.S. [1 ]
El-Rabaie E.-S.M. [1 ]
El-Shafai W. [1 ,2 ]
El Banby G.M. [3 ]
Alotaiby T. [4 ]
Alshebeili S.A. [5 ,6 ]
Abd El-Samie F.E. [1 ,7 ]
机构：
[1] Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf
[2] Security Engineering Laboratory, Department of Computer Science, Prince Sultan University, Riyadh
[3] Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf
[4] KACST, Riyadh
[5] Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh
[6] Department of Electrical Engineering, King Saud University, Riyadh
[7] Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh
来源：
Annals of Data Science | 2022年 / 9卷 / 02期
关键词：
EEG; Epilepsy; Seizure detection; Seizure prediction; SIFT;
D O I：
10.1007/s40745-020-00308-7
中图分类号：
学科分类号：
摘要：
Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal processing. The first one is an automatic seizure detection framework based on the utilization of scale-invariant feature transform (SIFT) as an extraction tool. The second one depends on the utilization of the fast Fourier transform (FFT) and an artificial neural network for epileptic seizure prediction. Finally, an automated patient-specific framework for channel selection and seizure prediction is presented based on FFT. The simulation results show the success of the proposed frameworks for automated medical diagnosis. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码：393 / 428
页数：35
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