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Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network

Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network

Ratnaprabha Ravindra Pune Borhade, Manoj S. Nagmode
Copyright: © 2021 |Volume: 12 |Issue: 3 |Pages: 19
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799860280|DOI: 10.4018/IJACI.2021070108
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MLA

Borhade, Ratnaprabha Ravindra Pune, and Manoj S. Nagmode. "Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network." IJACI vol.12, no.3 2021: pp.166-184. http://doi.org/10.4018/IJACI.2021070108

APA

Borhade, R. R. & Nagmode, M. S. (2021). Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network. International Journal of Ambient Computing and Intelligence (IJACI), 12(3), 166-184. http://doi.org/10.4018/IJACI.2021070108

Chicago

Borhade, Ratnaprabha Ravindra Pune, and Manoj S. Nagmode. "Epileptic Seizure Prediction Using Exponential Squirrel Atom Search Optimization-Based Deep Recurrent Neural Network," International Journal of Ambient Computing and Intelligence (IJACI) 12, no.3: 166-184. http://doi.org/10.4018/IJACI.2021070108

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Abstract

Electroencephalogram (EEG) signal is broadly utilized for monitoring epilepsy and plays a key role to revitalize close loop brain. The classical method introduced to find the seizures relies on EEG signals which is complex as well as costly, if channel count increases. This paper introduces the novel method named exponential-squirrel atom search optimization (Exp-SASO) algorithm in order to train the deep RNN for discovering epileptic seizure. Here, the input EEG signal is given to the pre-processing module for enhancing the quality of image by reducing the noise. Then, the pre-processed image is forwarded to the feature extraction module. The features, like statistical features, spectral features, logarithmic band power, wavelet coefficients, common spatial patterns, along with spectral decrease, pitch chroma, tonal power ratio, and spectral flux, are extracted. Once the features are extracted, the feature selection is carried out using fuzzy information gain model for choosing appropriate features for further processing.

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