TY  - CONF
ID  - scholars15464
TI  - Identification of Epileptic Seizures using Autoencoders and Convolutional Neural Network
KW  - Convolution; Convolutional neural networks; Diagnosis; Neurophysiology
KW  -  Auto encoders; Convolutional neural network; Data set; Epileptic seizures; Intracranial EEG; Manual intervention; Paper analysis; Prediction modelling; Seizure prediction; Stacked autoencoder
KW  -  Learning systems
N2  - Contemporary application of machine learning has paved a way for the medical diagnosis automation without any manual intervention. Once such application is early deduction of the epileptic seizures. Earlier identification of seizures aids specialists towards diagnosis. This paper analyzes on the detection of EEG epileptic seizures using Autoencoders, Convolutional Neural Network (CNN), and a multi class Stacked Autoencoder-CN model. These prediction models were analyzed on the intracranial EEG data set from15 real time patients, CHB-MIT dataset and P300 dataset. The results in python, proved for Stacked Autoencoder-Convolution Neural (SAE-CN) model to give optimum and effective solution in terms of higher speed and reduction in training time of the classifier and better probability of 0.925. This analysis proposes the idea of pre-prepared systems for other EEGrelated applications. © 2021 IEEE.
N1  - cited By 0; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661
AV  - none
A1  - Divya, P.
A1  - Aruna Devi, B.
A1  - Prabakar, S.
A1  - Porkumaran, K.
A1  - Kannan, R.
A1  - Nor, N.B.M.
A1  - Elamvazuthi, I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124149874&doi=10.1109%2fICIAS49414.2021.9642570&partnerID=40&md5=89497753288aa6a94a71584f390ba27f
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728176666
Y1  - 2021///
ER  -