EEG Transient Event Detection and Classification Using Association Rules Export

@article{citeulike:2948861, 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.}, author = {Exarchos, T. P. and Tzallas, A. T. and Fotiadis, D. I. and Konitsiotis, S. and Giannopoulos, S.}, booktitle = {Information Technology in Biomedicine, IEEE Transactions on}, citeulike-article-id = {2948861}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TITB.2006.872067}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1650497}, doi = {10.1109/TITB.2006.872067}, journal = {Information Technology in Biomedicine, IEEE Transactions on}, keywords = {artefact, calculation, eeg}, number = {3}, pages = {451--457}, posted-at = {2008-07-01 17:58:09}, priority = {2}, title = {EEG Transient Event Detection and Classification Using Association Rules}, url = {http://dx.doi.org/10.1109/TITB.2006.872067}, volume = {10}, year = {2006} }

TY - JOUR ID - citeulike:2948861 L3 - citeulike-article-id:2948861 N2 - 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. IS - 3 JF - Information Technology in Biomedicine, IEEE Transactions on EP - 457 TI - EEG Transient Event Detection and Classification Using Association Rules VL - 10 T2 - Information Technology in Biomedicine, IEEE Transactions on SP - 451 KW - artefact KW - calculation KW - eeg AU - Exarchos, TP AU - Tzallas, AT AU - Fotiadis, DI AU - Konitsiotis, S AU - Giannopoulos, S PY - 2006/// UR - http://dx.doi.org/10.1109/TITB.2006.872067 ER -