Nonlinear Noise Reduction and Predictability of Epileptic Seizures

Rajeshkumar Venugopal, Awadhesh Prasad, Narayanan Krishnamurthi, Andreas Spanias, L. D. Iasemidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this work we address the problem of nonlinear filtering of noise in EEG (electroencephalographic) signal using the centroid correction method, in an attempt to improve the predictability of epileptic seizures long prior to their occurrences. We analyzed multi-channel EEG recordings containing 15 successive seizures in one epileptic patient with focal temporal lobe epilepsy. The total EEG spanned 3 days. The results show an improvement in the predictability time in most of the seizures in addition to improvement in the number of predictable seizures (from 10 to 12) after filtering and dynamical analysis of the EEG.

Original languageEnglish (US)
Title of host publicationProceedings of the IASTED International Conference on Modelling and Simulation
EditorsM.H. Hamza, M.H. Hamza
Pages240-245
Number of pages6
StatePublished - Dec 1 2003
EventProceedings of the IASTED International Conference on Modelling and Simulation - Palm Springs, CA, United States
Duration: Feb 24 2003Feb 26 2003

Publication series

NameProceedings of the IASTED International Conference on Modelling and Simulation

Other

OtherProceedings of the IASTED International Conference on Modelling and Simulation
CountryUnited States
CityPalm Springs, CA
Period2/24/032/26/03

Keywords

  • Biomedical signals
  • EEG
  • Nonlinear noise reduction
  • Seizure predictability
  • Time series analysis

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Nonlinear Noise Reduction and Predictability of Epileptic Seizures'. Together they form a unique fingerprint.

Cite this