Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy

Iqra Tahir; Usman Qamar; Hassan Abbas; Babar Zeb; Sana Abid

doi:10.1145/3318299.3318374

Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy

Iqra Tahir
, Usman Qamar
, Hassan Abbas
, Babar Zeb
, Sana Abid

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.

Original language	English
Title of host publication	ACM International Conference Proceeding Series
Publisher	Association for Computing Machinery
Pages	197-203
Number of pages	7
ISBN (Print)	9781450366007
DOIs	https://doi.org/10.1145/3318299.3318374
State	Published - 2019
Externally published	Yes
Event	11th International Conference on Machine Learning and Computing, ICMLC 2019 - Zhuhai, China Duration: 22 Feb 2019 → 24 Feb 2019

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F148150

Conference

Conference	11th International Conference on Machine Learning and Computing, ICMLC 2019
Country/Territory	China
City	Zhuhai
Period	22/02/19 → 24/02/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

EEG signals
Electroencephalogram
Epilepsy
IPSO
Neural Networks
Seizure

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3318299.3318374

Cite this

Tahir, I., Qamar, U., Abbas, H., Zeb, B., & Abid, S. (2019). Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy. In ACM International Conference Proceeding Series (pp. 197-203). (ACM International Conference Proceeding Series; Vol. Part F148150). Association for Computing Machinery. https://doi.org/10.1145/3318299.3318374

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title = "Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy",

abstract = "EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 \% is reported for a publicly available dataset.",

keywords = "EEG signals, Electroencephalogram, Epilepsy, IPSO, Neural Networks, Seizure",

author = "Iqra Tahir and Usman Qamar and Hassan Abbas and Babar Zeb and Sana Abid",

note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 11th International Conference on Machine Learning and Computing, ICMLC 2019 ; Conference date: 22-02-2019 Through 24-02-2019",

year = "2019",

doi = "10.1145/3318299.3318374",

language = "English",

isbn = "9781450366007",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "197--203",

booktitle = "ACM International Conference Proceeding Series",

}

Tahir, I, Qamar, U, Abbas, H, Zeb, B & Abid, S 2019, Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy. in ACM International Conference Proceeding Series. ACM International Conference Proceeding Series, vol. Part F148150, Association for Computing Machinery, pp. 197-203, 11th International Conference on Machine Learning and Computing, ICMLC 2019, Zhuhai, China, 22/02/19. https://doi.org/10.1145/3318299.3318374

Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy. / Tahir, Iqra; Qamar, Usman; Abbas, Hassan et al.
ACM International Conference Proceeding Series. Association for Computing Machinery, 2019. p. 197-203 (ACM International Conference Proceeding Series; Vol. Part F148150).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy

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AU - Qamar, Usman

AU - Abbas, Hassan

AU - Zeb, Babar

AU - Abid, Sana

PY - 2019

Y1 - 2019

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AB - EEG signal classification is a pivotal task for identification of different brain related disorders. The paper is about classification of EEG signal presenting a novel approach for the identification of whether the seizure is epileptic or normal that technique is based on training of neural network with having improved simplified swarm optimization algorithm. Our proposed methodology is evaluated with different parameters and testing accuracy of 94 % is reported for a publicly available dataset.

KW - EEG signals

KW - Electroencephalogram

KW - Epilepsy

KW - IPSO

KW - Neural Networks

KW - Seizure

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DO - 10.1145/3318299.3318374

M3 - Conference contribution

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SN - 9781450366007

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PB - Association for Computing Machinery

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ER -

Classification of EEG signal by training neural network with swarm optimization for identification of epilepsy

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this