Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods-Reference-Cited by-同舟云学术

Detection and classification of electroencephalogram signals for epilepsy disease using machine learning methods

Published:2020-09-06 Issue:2 Volume:31 Page:729-740
ISSN:0899-9457
Container-title:International Journal of Imaging Systems and Technology
language:en
Short-container-title:Int J Imaging Syst Technol

Author:

Srinath Rajagopalan¹^ORCID,Gayathri Rajagopal²

Affiliation:

1. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi, Chennai India

2. Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Pennalur, Sriperumbudur India

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ima.22486

Reference27 articles.

1. Classification of epileptic EEG signals using time‐delay neural networks and probabilistic neural networks;Goshvarpour A;Int J Inf Eng Electron Business,2013

2. Nonrandomness, nonlinear dependence, and non‐stationarity of electroencephalographic recordings from epilepsy patients;Andrzejak RG;Phys Rev,2012

3. Automated diagnosis of epilepsy using key‐point based local binary pattern of EEG signals;Tiwari A;IEEE J Biomed Health Inform,2016

4. The detection of epileptic seizure signals based on fuzzy entropy

5. Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals

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