Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

Document Type

Article

Source of Publication

Journal of Ambient Intelligence and Humanized Computing

Publication Date

1-1-2020

Abstract

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results.

ISSN

1868-5137

Publisher

Springer Science and Business Media Deutschland GmbH

Last Page

12

Disciplines

Computer Sciences

Keywords

Artificial alga optimized general adversarial networks, Brain informatics, Electroencephalogram (EEG), Epilepsy, Krill herd algorithm

Scopus ID

85091182585

Indexed in Scopus

yes

Open Access

no

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