Using Clinical Information and Nonlinear EEG Analysis for Diagnosis of Dementia

Maurice E. Cohen, Donna L. Hudson, Fen Lei Chang, Mark Kramer

Research output: Contribution to journalConference article

Abstract

The analysis of electrocardiograms presents a particularly difficult problem due to a number of factors, including the lack of specificity of the signal and the inability to map the scalp potential to physiological parameters. In the work described here, nonlinear EEG analysis based on computation of cortical potential followed by nonlinear analysis based on the computation of degree of variability is combined with imaging results and clinical parameters to form a diagnostic model for dementia diagnosis. These parameters are combined through the use of an intelligent agent model that uses a knowledge-based system and a neural network model in addition to the biomedical signal analyzer.

Original languageEnglish (US)
Pages (from-to)2299-2302
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

Keywords

  • Chaotic analysis
  • Cortical potential
  • Dementia
  • Intelligent agents
  • Neural networks

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Using Clinical Information and Nonlinear EEG Analysis for Diagnosis of Dementia. / Cohen, Maurice E.; Hudson, Donna L.; Chang, Fen Lei; Kramer, Mark.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, Vol. 3, 01.12.2003, p. 2299-2302.

Research output: Contribution to journalConference article

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