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Wavelet-Chaos-Neural Network Models for EEG-Based Diagnosis of Neurological Disorders

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6485)

Abstract

In this Keynote Lecture an overview of the author’s research for automated electroencephalogram (EEG)-based diagnosis of neurological disorders is presented. Sample research and wavelet-chaos-neural network models developed by the author and his research associates in recent years for diagnosis of epilepsy, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and the Alzheimer’s Disease (AD) are reviewed briefly. The significant impact of this research on the future of neurology practice and its ramification are discussed.

Keywords

  • Autism Spectrum Disorder
  • Attention Deficit Hyperactivity Disorder
  • Neural System
  • Radial Basis Function Neural Network
  • Seizure Detection

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Adeli, H. (2010). Wavelet-Chaos-Neural Network Models for EEG-Based Diagnosis of Neurological Disorders. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_1

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