Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction

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 () and Fathi E. Abd El-Samie ()
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Heba M. Emara: Menoufia University
Mohamed Elwekeil: Menoufia University
Taha E. Taha: Menoufia University
Adel S. El-Fishawy: Menoufia University
El-Sayed M. El-Rabaie: Menoufia University
Walid El-Shafai: Menoufia University
Ghada M. El Banby: Menoufia University
Turky Alotaiby: KACST
Saleh A. Alshebeili: King Saud University
Fathi E. Abd El-Samie: Menoufia University

Annals of Data Science, 2022, vol. 9, issue 2, No 12, 393-428

Abstract: 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.

Keywords: Epilepsy; EEG; SIFT; Seizure prediction; Seizure detection. (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40745-020-00308-7

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