Epileptic Seizure Recognition Using Reduced Deep Convolutional Stack Autoencoder and Improved Kernel RVFLN From EEG Signals

In this paper, reduced deep convolutional stack autoencoder (RDCSAE) and improved kernel random vector functional link network (IKRVFLN) are combined to recognize the epileptic seizure using both the multichannel scalp and single-channel electroencephalogram (EEG) signals. The novel RDCSAE structure...

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Bibliographic Details
Published in:	IEEE transactions on biomedical circuits and systems Vol. 15; no. 3; pp. 595 - 605
Main Authors:	Sahani, Mrutyunjaya, Rout, Susanta Kumar, Dash, Pradipta Kishor
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Artificial neural networks CAD Computer aided design Computer applications Convolution Convulsions & seizures Cost function Diagnosis EEG Electroencephalography Epilepsy Epileptic seizure Feature extraction Field programmable gate arrays improved kernel random vector functional link network Kernel Kernels multichannel scalp electroencephalogram Neural networks Recognition reduced deep convolutional stack autoencoder Scalp Seizures single-channel electroencephalogram Support vector machines Wavelet packets
ISSN:	1932-4545, 1940-9990, 1940-9990
Online Access:	Get full text
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