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Annals of Data Science
Published in: Annals of Data Science 2/2022

07-06-2021

Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

Authors: Heba M. Emara, Mohamed Elwekeil, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Walid El-Shafai, Ghada M. El Banby, Turky Alotaiby, Saleh A. Alshebeili, Fathi E. Abd El-Samie

Published in: Annals of Data Science | Issue 2/2022

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Abstract

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.
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Metadata
Title
Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction
Authors
Heba M. Emara
Mohamed Elwekeil
Taha E. Taha
Adel S. El-Fishawy
El-Sayed M. El-Rabaie
Walid El-Shafai
Ghada M. El Banby
Turky Alotaiby
Saleh A. Alshebeili
Fathi E. Abd El-Samie
Publication date
07-06-2021
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 2/2022
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-020-00308-7

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