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62768

Sensitivity of Seizure Pattern Prediction to EEG Signal Compression

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Last updated: 04 Jan 2025

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Abstract

This paper presents a framework for Electroencephalography (EEG) seizure prediction in time domain. Moreover, it studies an efficient lossy EEG signal compression technique and its effect on further processing for seizure prediction in a realistic signal acquisition and compression scenario. Compression of EEG signals are one of the most important solutions in saving speed up signals transfer, reduction of energy transmission and the required memory for storage in addition to reduction costs for storage hardware and network bandwidth. The main objective of this research is to use trigonometric compression techniques including; Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) algorithms on EEG signals and study the impact of the reconstructed EEG signals on its seizure prediction ability. Simulation results show that the DCT achieves the best prediction results compared with DST technique achieving sensitivity of 95.238% and 85.714% respectively. The proposed approach gives longer prediction times compared to traditional EEG seizure prediction approaches. Therefore, it will help specialists for the prediction of epileptic seizure as earlier as possible.

DOI

10.21608/mjeer.2019.62768

Authors

First Name

Sally

Last Name

El-Gindy

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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First Name

Sami

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El-Dolil

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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First Name

Adel

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El-Fishawy

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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First Name

El-Sayed

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El-Rabaie

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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First Name

Moawad

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Dessouky

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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Fathi

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Abd El-Samie

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Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt.

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First Name

Turky

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Elotaiby

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KACST, Kingdom of Saudi Arabia Dept.

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First Name

Saleh

Last Name

Elshebeily

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Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University.

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Volume

28

Article Issue

2

Related Issue

9507

Issue Date

2019-07-01

Receive Date

2018-12-09

Publish Date

2019-07-01

Page Start

97

Page End

116

Print ISSN

1687-1189

Online ISSN

2682-3535

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https://mjeer.journals.ekb.eg/article_62768.html

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https://mjeer.journals.ekb.eg/service?article_code=62768

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7

Type

Original Article

Type Code

1,088

Publication Type

Journal

Publication Title

Menoufia Journal of Electronic Engineering Research

Publication Link

https://mjeer.journals.ekb.eg/

MainTitle

Sensitivity of Seizure Pattern Prediction to EEG Signal Compression

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Article

Created At

22 Jan 2023