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Data-Mining of Time-Domain Features from Neural Extracellular Field Data

  • Samuel Neymotin
  • Daniel J. Uhlrich
  • Karen A. Manning
  • William W. Lytton
Part of the Studies in Computational Intelligence book series (SCI, volume 151)

Summary

Spike-wave and polyspike-wave activity in electroencephalogram are waveforms typical of certain epileptic states. Automated detection of such patterns would be desirable for automated seizure detection in both experimental and clinical venues. We have developed a time-domain algorithm denominated SPUD to facilitate data-mining of large electroencephalogram/electrocorticogram datasets to identify the occurrence of spike-wave or other activity patterns. This algorithm feeds into our enhanced Neural Query System [2, 12] database application to facilitate data-mining. We have used our algorithm to identify and classify activity from both simulated and experimental seizures.

Keywords

Epileptiform Activity Feature Extraction Algorithm Threshold Line Logarithmic Spacing Small Bump 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Samuel Neymotin
    • 1
  • Daniel J. Uhlrich
    • 2
  • Karen A. Manning
    • 2
  • William W. Lytton
    • 1
  1. 1.Dept. Biomedical EngineeringSUNY Downstate Medical CenterBrooklyn, NY 
  2. 2.Dept. of AnatomyUniversity of WisconsinMadison, WI 

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