Clustering Electroencephalogram Recordings to Study Mesial Temporal Lobe Epilepsy - Clustering Challenges in Biological Network - page 265

Chapter 14
Clustering Electroencephalogram Recordings to Study Mesial
Temporal Lobe Epilepsy
Chang-Chia Liu
Department of Industrial and Systems Engineering
Department of Biomedical Engineering, University of Florida, USA
[e-mail]
Wichai Suharitdamrong
Department of Industrial and Systems Engineering, University of Florida, USA
[e-mail]
W. Art Chaovalitwongse
Department of Industrial and Systems Engineering, Rutgers University, USA
[e-mail]
Georges A. Ghacibeh
Northeast Regional Epilepsy Group, 20 Prospect Ave, Suite 800, Hackensack
NJ, 07601, USA
[e-mail]
Panos M. Pardalos
Department of Industrial and Systems Engineering
Department of Biomedical Engineering, University of Florida, USA
[e-mail]
The brain connectivity is known to have substantial influences over the brain
function and its underlying information processes. In this chapter, a novel graph-
theoretic approach is introduced to investigate the connectivity among brain re-
gions through electroencephalogram (EEG) recordings acquired from a patient
with mesial temporal lobe epilepsy (MTLE). The first step of the proposed ap-
proach is to transform the brain connectivity behavior into a complete graph. The
connectivity for each pair of the brain regions is first quantified by the cross mu-
tual information (CMI) measure, and then the maximum clique algorithm is sub-
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