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Automatic Detection of Critical Epochs in coma-EEG Using Independent Component Analysis and Higher Order Statistics

  • G. Inuso
  • F. La Foresta
  • N. Mammone
  • F. C. Morabito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Previous works showed that the joint use of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) allows to extract a few meaningful dominant components from the EEG of patients in coma. A procedure for automatic critical epoch detection might support the doctor in the long time monitoring of the patients, this is why we are headed to find a procedure able to automatically quantify how much an epoch is critical or not. In this paper we propose a procedure based on the extraction of some features from the dominant components: the entropy and the kurtosis. This feature analysis allowed us to detect some epochs that are likely to be critical and that are worth inspecting by the expert in order to assess the possible restarting of the brain activity.

Keywords

Principal Component Analysis Independent Component Analysis Brain Death Automatic Detection Independent Component Analysis 
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 2006

Authors and Affiliations

  • G. Inuso
    • 1
  • F. La Foresta
    • 1
  • N. Mammone
    • 1
  • F. C. Morabito
    • 1
  1. 1.DIMET – Mediterranea University of Reggio CalabriaReggio CalabriaItaly

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