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EEG transient event detection and classification using association rules.

Authors
  • Exarchos, Themis P
  • Tzallas, Alexandros T
  • Fotiadis, Dimitrios I
  • Konitsiotis, Spiros
  • Giannopoulos, Sotirios
Type
Published Article
Journal
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society
Publication Date
Jul 01, 2006
Volume
10
Issue
3
Pages
451–457
Identifiers
PMID: 16871711
Source
Medline
License
Unknown

Abstract

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.

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