EEG transient event detection and classification using association rules.
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.
Exarchos
Themis P
TP
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Greece. me01238@cc.uoi.gr
Tzallas
Alexandros T
AT
Fotiadis
Dimitrios I
DI
Konitsiotis
Spiros
S
Giannopoulos
Sotirios
S
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000503:physiopathology*; D016000:Cluster Analysis; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors