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EEG Transient Event Detection and Classification Using Association Rules

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5 Author(s)
Exarchos, T.P. ; Unit of Med. Technol. & Intelligent Inf. Syst., Univ. of Ioannina ; Tzallas, A.T. ; Fotiadis, D.I. ; Konitsiotis, S.
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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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Information Technology in Biomedicine, IEEE Transactions on  (Volume:10 ,  Issue: 3 )