International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1261 - 1269

Hybrid Dragonfly Optimization-Based Artificial Neural Network for the Recognition of Epilepsy

Authors
K. G. Parthiban1, *, S. Vijayachitra2, R. Dhanapal3
1Department of Electronic and Communication Engineering, Adithya Institute of Technology, Coimbatore City, India – 641 107
2Department of Electronic and Instrumentation Engineering, Kongu Engineering College, Erode City, India – 638 060
3Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore City, India – 641 021
*Corresponding author. Email: parthipankgp@gmail.com
Corresponding Author
K. G. Parthiban
Received 22 June 2019, Accepted 10 September 2019, Available Online 15 November 2019.
DOI
10.2991/ijcis.d.191022.001How to use a DOI?
Keywords
Electroencephalography; Kalman filter; Variable mode decomposition; Modified principal component analysis; Artificial neural network; Hybrid dragonfly algorithm
Abstract

Epilepsy can well be stated as a disorder of the central nervous systems (CNS) that brought about recurring seizures owing to chronic abnormal blasts of electrical discharge on the brain. Knowing if an individual is having a seizure and diagnosing the seizure type or epilepsy syndrome could be hard. Many methods were developed to recognize this disease. But the existing techniques for detection of epilepsy are not satisfied with accuracy, and cannot identify the diseases effectively. To trounce these drawbacks, this paper proposes an approach for the recognition of Epilepsy as of the electroencephalography (EEG) signals. This is implemented as follows. Primarily, the Kalman filter (KF) is utilized for pre-processing to eradicate the impulse noise present in the EEG signals. This filtered signal is then decomposed utilizing variable modes decomposition (VMD). Feature extraction (FE) is performed by computing 7 features. The dimensionality of this signal is then lessened using Modified-Principal Components Analysis (M-PCA). Finally, classification is conducted utilizing the artificial neural networks (ANN) that is optimized using the hybrid dragonfly algorithm (HDA). Disparate performance metrics such as sensitivity, accuracy, and false discovery rates (FDR) are ascertained and as well weighted against with the existent works.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1261 - 1269
Publication Date
2019/11/15
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191022.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - K. G. Parthiban
AU  - S. Vijayachitra
AU  - R. Dhanapal
PY  - 2019
DA  - 2019/11/15
TI  - Hybrid Dragonfly Optimization-Based Artificial Neural Network for the Recognition of Epilepsy
JO  - International Journal of Computational Intelligence Systems
SP  - 1261
EP  - 1269
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191022.001
DO  - 10.2991/ijcis.d.191022.001
ID  - Parthiban2019
ER  -