Journal Published Online: 20 February 2020
Volume 49, Issue 4

Classification of Epilepsy Types from Electroencephalogram Time Series Using Continuous Wavelet Transform Scalogram–Based Convolutional Neural Network

CODEN: JTEVAB

Abstract

During the supervisory activities of the brain, the electrical activities of nerve cell clusters produce oscillations. These complex biopotential oscillations are called electroencephalogram (EEG) signals. Certain diseases, such as epilepsy, can be detected by measuring these signals. Epilepsy is a disease that manifests itself as seizures. These seizures manifest themselves in different characteristics. These different characteristics divide epilepsy seizure types into two main groups: generalized and partial epilepsy. This study aimed to classify different types of epilepsy from EEG signals. For this purpose, a scalogram-based, deep learning approach has been developed. The utilized classification process had the following main steps: the scalogram images were obtained by using the continuous wavelet transform (CWT) method. So, a one-dimension EEG time series was converted to a two-dimensional time-frequency data set in order to extract more features. Then, the increased dimension data set (CWT scalogram images) was applied to the convolutional neural network (CNN) as input patterns for classifying the images. The EEG signals were taken from Dicle University, Neurology Clinic of Medical School. This data consisted of four classes: healthy brain waves, generalized preseizure, generalized seizure, and partial epilepsy brain waves. With the proposed method, the average accuracy performance of three of the EEG records’ classes (healthy, generalized preseizure, and generalized seizure), and that of all four classes of EEG records were 90.16 % (± 0.20) and 84.66 % (± 0.48). According to these results, regarding the specific accuracy ratings of the recordings, the healthy EEG records scored 91.29 %, generalized epileptic seizure records were at 96.50 %, partial seizure EEG records scored 89.63 %, and the preseizure EEG records had a 90.44 % rating. The results of the proposed method were compared to the results of both similar studies and conventional methods. As a result, the performance of the proposed method was found to be acceptable.

Author Information

Türk, Ömer
Department of Computer Programming, Mardin Artuklu University, Mardin, Turkey
Akpolat, Veysi
Department of Biophysics, Faculty of Medicine, Dicle University, Diyarbakır, Turkey
Varol, Sefer
Departments of Neurology,Faculty of Medicine, Dicle University, Diyarbakır, Turkey
Aluçlu, Mehmet Ufuk
Departments of Neurology,Faculty of Medicine, Dicle University, Diyarbakır, Turkey
Özerdem, Mehmet Siraç
Department of Electrical and Electronics Engineering Dicle University, Diyarbakır, Turkey
Pages: 16
Price: $25.00
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Details
Stock #: JTE20190626
ISSN: 0090-3973
DOI: 10.1520/JTE20190626