EEG transient event detection and classification using association rules.

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.