Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy

Chang Chia Liu, Wichai Suharitdamrong, W. Art Chaovalitwongse, Georges A. Ghacibeh, Panos M. Pardalos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The brain connectivity is known to have substantial influences over the brain function and its underlying information processes. In this chapter, a novel graphtheoretic approach is introduced to investigate the connectivity among brain regions through electroencephalogram (EEG) recordings acquired from a patient with mesial temporal lobe epilepsy (MTLE). The first step of the proposed approach 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 mutual information (CMI) measure, and then the maximum clique algorithm is subsequently applied to find the clique that contained a group of highly connected brain regions that is represented by a clique with maximum size. The CMI is known to have the ability to capture the connectivity between EEG signals. The adopted maximum clique algorithm can reduce the complexity of the clustering procedure for finding the maximum connected brain regions. The proposed graph-theoretic approach offers better assessments to visualize the structure of the brain connectivity over time. The results indicate that the maximum connected brain regions prior to seizure onsets were where the impending seizure was initiated. Furthermore, the proposed approach may be used to improve the outcome of the epilepsy surgery by identifying the seizure onset region(s) correctly.

Original languageEnglish (US)
Title of host publicationClustering Challenges in Biological Networks
Pages267-280
Number of pages14
StatePublished - 2013
Externally publishedYes
EventDIMACS Workshop on Clustering Problems in Biological Networks 2009 - Piscataway, NJ, United States
Duration: May 9 2006May 11 2006

Other

OtherDIMACS Workshop on Clustering Problems in Biological Networks 2009
CountryUnited States
CityPiscataway, NJ
Period5/9/065/11/06

Fingerprint

epilepsy
electroencephalography
lobes
brain
recording
seizures
surgery

ASJC Scopus subject areas

  • Nuclear and High Energy Physics

Cite this

Liu, C. C., Suharitdamrong, W., Chaovalitwongse, W. A., Ghacibeh, G. A., & Pardalos, P. M. (2013). Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy. In Clustering Challenges in Biological Networks (pp. 267-280)

Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy. / Liu, Chang Chia; Suharitdamrong, Wichai; Chaovalitwongse, W. Art; Ghacibeh, Georges A.; Pardalos, Panos M.

Clustering Challenges in Biological Networks. 2013. p. 267-280.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, CC, Suharitdamrong, W, Chaovalitwongse, WA, Ghacibeh, GA & Pardalos, PM 2013, Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy. in Clustering Challenges in Biological Networks. pp. 267-280, DIMACS Workshop on Clustering Problems in Biological Networks 2009, Piscataway, NJ, United States, 5/9/06.
Liu CC, Suharitdamrong W, Chaovalitwongse WA, Ghacibeh GA, Pardalos PM. Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy. In Clustering Challenges in Biological Networks. 2013. p. 267-280
Liu, Chang Chia ; Suharitdamrong, Wichai ; Chaovalitwongse, W. Art ; Ghacibeh, Georges A. ; Pardalos, Panos M. / Clustering electroencephalogram recordings to study mesial temporal lobe epilepsy. Clustering Challenges in Biological Networks. 2013. pp. 267-280
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