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

  • Recent Advances in Deep Learning for Medical Image Processing
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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.

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Authors and Affiliations

  1. Department of Computer Engineering, Engineering Faculty, Munzur University, 62100, Tunceli, Turkey

    Özal Yıldırım & Ulas Baran Baloglu

  2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Biomedical Engineering, School of Science and Technology, Singapore School of Social Sciences, Singapore, Singapore

    U. Rajendra Acharya

  4. Faculty of Health and Medical Sciences, School of Medicine, Taylor’s University, 47500, Subang Jaya, Malaysia

    U. Rajendra Acharya

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  1. Özal Yıldırım
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  2. Ulas Baran Baloglu
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  3. U. Rajendra Acharya
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Correspondence to Özal Yıldırım.

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Yıldırım, Ö., Baloglu, U.B. & Acharya, U.R. A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput & Applic 32, 15857–15868 (2020). https://doi.org/10.1007/s00521-018-3889-z

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