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A deep convolutional neural network model for automated identification of abnormal EEG signals

  • Özal Yıldırım
  • Ulas Baran Baloglu
  • U. Rajendra Acharya
Recent Advances in Deep Learning for Medical Image Processing
  • 85 Downloads

Abstract

Electroencephalogram (EEG) is widely used to monitor the brain activities. The manual examination of these signals by experts is strenuous and time consuming. Hence, machine learning techniques can be used to improve the accuracy of detection. Nowadays, deep learning methodologies have been used in medical field to diagnose the health conditions precisely and aid the clinicians. In this study, a new deep one-dimensional convolutional neural network (1D CNN) model is proposed for the automatic recognition of normal and abnormal EEG signals. The proposed model is a complete end-to-end structure which classifies the EEG signals without requiring any feature extraction. In this study, we have used the EEG signals from temporal to occipital (T5–O1) single channel obtained from Temple University Hospital EEG Abnormal Corpus (v2.0.0) EEG dataset to develop the 1D CNN model. Our developed model has yielded the classification error rate of 20.66% in classifying the normal and abnormal EEG signals.

Keywords

Convolutional neural network Abnormal EEG EEG classification Deep learning 

Notes

Compliance with ethical standards

Conflict of interest

There is no conflict of interest in this work.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Engineering FacultyMunzur UniversityTunceliTurkey
  2. 2.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  3. 3.Department of Biomedical Engineering, School of Science and TechnologySingapore School of Social SciencesSingaporeSingapore
  4. 4.Faculty of Health and Medical Sciences, School of MedicineTaylor’s UniversitySubang JayaMalaysia

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