Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Automatic epileptic seizure detection using SVM techniques with EEG signals

Authors
J. Vidya1, *, P. Padmini Rani1, Ebraheem Khaleelullah Shaik2, Tahera Inkollu3, Meghana Gurram3, Kavya Bommina3, Kusuma Sri3
1Assistant Professor, Department of CSE, Vignan’s Lara Institute of Technology & Science, Vadlamudi, Guntur, Andhra Pradesh, India
2Assistant Professor, Department of CSE, Narsaraopeta Engineering College, Narsaraopeta, Guntur, Andhra Pradesh, India
3UG Final Year, Department of CSE, Vignan’s Lara Institute of Technology & Science, Vadlamudi, Guntur, Andhra Pradesh, India
*Corresponding author. Email: Vidyajyothula9@gmail.com
Corresponding Author
J. Vidya
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_83How to use a DOI?
Keywords
Epileptic seizure; Brain; Eletroencephalogram (EEG); Support Vector Machinne (SVM)
Abstract

Epileptic seizures, the Manifestation of abnormal electrical activity in the brain, represents a significant challenge in neurological health. Epileptic Seizures is unpredictable nature of when they occur, leading to potential injury or danger during this episode and can disrupt daily activities. Available existing methodologies using electroencephalography (EEG) which monitors brain activity through applied of electrodes to the scalp. Most of the researchers developed mechanized technologies for EEG-based system for prediction of epileptical seizure using AI methodologies, limited by high error value, high accuracy, time saving and peak efficiency. Proposed a EEG-based method using SVM classifier for increasing prediction rate of epileptic Seizure. As a result, using SVM algorithm obtained 92% of accuracy.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_83
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_83How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - J. Vidya
AU  - P. Padmini Rani
AU  - Ebraheem Khaleelullah Shaik
AU  - Tahera Inkollu
AU  - Meghana Gurram
AU  - Kavya Bommina
AU  - Kusuma Sri
PY  - 2024
DA  - 2024/07/30
TI  - Automatic epileptic seizure detection using SVM techniques with EEG signals
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 876
EP  - 883
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_83
DO  - 10.2991/978-94-6463-471-6_83
ID  - Vidya2024
ER  -