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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals.

Ömer Türk1, Mehmet Siraç Özerdem2

  • 1Department of Computer programming, Mardin Artuklu University, Mardin 47500, Turkey. omerturk@artuklu.edu.tr.

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Summary

This study introduces a new deep learning method using Electroencephalogram (EEG) signals for automated disease detection. The approach achieves high accuracy in classifying epilepsy from EEG data, reducing the need for manual feature extraction.

Keywords:
Continuous Wavelet TransformConvolutional Neural NetworkEEGEpilepsyscalogram

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Electroencephalogram (EEG) signal analysis is crucial for brain-computer interfaces and disease diagnosis.
  • Traditional EEG analysis methods require extensive manual feature engineering, which is time-consuming and labor-intensive.
  • Deep learning offers a promising alternative for automated feature learning from complex biological signals like EEG.

Purpose of the Study:

  • To develop and evaluate a novel deep learning approach for automated EEG-based disease classification.
  • To investigate the efficacy of using 2D frequency-time scalograms derived from EEG signals.
  • To compare the performance of the proposed method against existing literature benchmarks using the University of Bonn dataset.

Main Methods:

  • Continuous Wavelet Transform was applied to EEG records to generate 2D frequency-time scalograms.
  • A Convolutional Neural Network (CNN) architecture was employed to learn features directly from these scalogram images.
  • The University of Bonn dataset, comprising five classes of EEG signals (healthy and epilepsy), was utilized for training and validation.

Main Results:

  • The proposed CNN model achieved high classification accuracies: 99.50% for A-E, 100% for A-D and B-D (binary classification).
  • Triple classification (A-D-E) reached 99.00%, quaternary classification (A-C-D-E, B-C-D-E) achieved 90.50% and 91.50% respectively.
  • The model demonstrated strong performance in five-class classification, with an accuracy of 93.60% for A-B-C-D-E.

Conclusions:

  • The proposed method effectively utilizes 2D scalograms and CNNs for automated EEG feature learning and classification.
  • This approach significantly reduces the reliance on manual feature extraction, offering a more efficient and cost-effective solution for disease detection.
  • The high accuracy rates demonstrate the potential of this deep learning strategy for clinical applications in neurological disorder diagnosis.