Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals

Author:

WeiKoh Joel En1,Rajinikanth Venkatesan2,Vicnesh Jahmunah1ORCID,Pham The‐Hanh1,Oh Shu Lih1,Yeong Chai Hong3,Sankaranarayanan Meena4,Kamath Aditya5,Bairy Giliyar Muralidhar5,Barua Prabal Datta67,Cheong Kang Hao8

Affiliation:

1. School of Engineering Ngee Ann Polytechnic Singapore Singapore

2. Department of Electronics and Instrumentation St. Joseph's College of Engineering Chennai India

3. School of Medicine, Faculty of Health and Medical Sciences Taylor's University Subang Jaya Malaysia

4. Department of Mathematics Anand Institute of Higher Technology Chennai India

5. Department of Biomedical Engineering Manipal Institute of Technology, Manipal Academy of Higher Education Manipal India

6. Faculty of Engineering and Information Technology University of Technology Sydney Australia

7. School of Management & Enterprise University of Southern Queensland Toowoomba Australia

8. Science, Mathematics and Technology Cluster Singapore University of Technology and Design Singapore Singapore

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference85 articles.

1. Abe S.Analysis of Multiclass Support Vector Machines. International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2003) 385–396 2003.

2. Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation;Aboalayo K. A. I.;Entropy,2016

3. Characterization of focal EEG signals: A review;Acharya U. R.;Future Generation Computer Systems,2019

4. Automated EEG‐based screening of depression using deep convolutional neural network;Acharya U. R.;Computer Methods and Programs in Biomedicine,2018

5. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals;Acharya U. R.;Computers in Biology and Medicine,2018

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