Next Article in Journal
Joint Modulation of Facial Expression Processing by Contextual Congruency and Task Demands
Previous Article in Journal
Evaluation of Postnatal Sedation in Full-Term Infants
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals

1
Department of Computer programming, Mardin Artuklu University, Mardin 47500, Turkey
2
Department of Electronics Engineering Dicle University, Diyarbakır 21100, Turkey
*
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(5), 115; https://doi.org/10.3390/brainsci9050115
Received: 27 March 2019 / Revised: 15 May 2019 / Accepted: 17 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Advances in EEG/ MEG Source Imaging)
  |  
PDF [2416 KB, uploaded 28 May 2019]
  |  

Abstract

The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%. View Full-Text
Keywords: Epilepsy; EEG; scalogram; Convolutional Neural Network; Continuous Wavelet Transform Epilepsy; EEG; scalogram; Convolutional Neural Network; Continuous Wavelet Transform
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Türk, Ö.; Özerdem, M.S. Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals. Brain Sci. 2019, 9, 115.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Brain Sci. EISSN 2076-3425 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top