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A natural evolution optimization based deep learning algorithm for neurological disorder classification.

Maha Shams1, Alaa Sagheer1,2

  • 1Center for Artificial Intelligence and Robotics (Cairo), Department of Computer Sciences, Aswan University, Egypt.

Bio-Medical Materials and Engineering
|June 1, 2020
PubMed
Summary

This study introduces a novel deep learning approach using natural evolution optimization for electroencephalography (EEG) signal classification. The method demonstrates superior performance in identifying neurological disorders like epilepsy and motor imagery.

Keywords:
Deep learningEEG signal classificationL1-principal component analysisartificial bee colonynatural evolution strategiesneurological disorder

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Machine Learning

Background:

  • Neurological disorders significantly impact brain and spinal cord functions.
  • Electroencephalography (EEG) monitoring is crucial for diagnosing brain disorders.
  • Robust automatic EEG signal classification aids early detection and intervention.

Purpose of the Study:

  • Introduce an effective automatic classification approach: natural evolution optimization-based deep learning (NEODL).
  • Integrate NEODL within a signal processing framework for automatic EEG classification.
  • Leverage natural evolution strategies (NES) for enhanced computational capabilities.

Main Methods:

  • EEG signal enhancement using L1-principal component analysis.
  • Wavelet transform for sub-band decomposition and feature extraction.
  • Artificial bee colony for optimal feature selection.
  • NEODL classifier for final signal classification.

Main Results:

  • The NEODL approach was evaluated on two benchmark datasets.
  • The classifier demonstrated superior performance compared to other deep learning and existing methods.
  • Applications included epilepsy disease and motor imagery classification.

Conclusions:

  • The NEODL framework shows promising performance for EEG signal classification.
  • Potential applications include identifying epileptogenic zones and use in clinical settings.
  • The approach is suitable for neuro-intensive care, epilepsy monitoring, and brain-computer interfaces.