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

Department of Computer programming, Mardin Artuklu University, Mardin 47500, Turkey
Department of Electronics Engineering Dicle University, Diyarbakır 21100, Turkey
Author to whom correspondence should be addressed.
Brain Sci. 2019, 9(5), 115;
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]


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

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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).

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

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