Automatic Detection of Epileptiform Discharges in EEG Using a Back-Propagation Network

Abstract

Abstract: This paper presents an automatic approach to detect epileptiform discharges (ED) in electroencephalogram (EEG). On the algorithm we utilized back-propagation artificial neural network (BPN) to detect ED. We train BPN respectively for each patient and induce parameter k to determine a threshold value. The result shows that the algorithm can determine presence or absence of ED automatically, and decrease the false determination in current automated approaches as well.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410608

Entities

People

  • Feng Xie
  • Shupeng Liu
  • Zhuangzhi Yan

Organizations

  • Shanghai University

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artifacts
  • Automatic
  • Biomedical Engineering
  • Computers
  • Detection
  • Distribution Curves
  • Electroencephalography
  • Electronic Mail
  • Engineering
  • Military Research
  • Neural Networks
  • Neurology
  • Neurophysiology
  • Peak Values
  • Recognition

Fields of Study

  • Engineering

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Mental Health of Military Veterans with Posttraumatic Stress Disorder (PTSD): Risk Factors, Prevalence, Symptoms, and Treatment.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks