dc.contributor.author |
Nasser, Hassan Ali, |
dc.date.accessioned |
2018-10-11T11:36:42Z |
dc.date.available |
2018-10-11T11:36:42Z |
dc.date.issued |
2018 |
dc.date.submitted |
2018 |
dc.identifier.other |
b22051338 |
dc.identifier.uri |
http://hdl.handle.net/10938/21323 |
dc.description |
Thesis. M.E. American University of Beirut. Department of Electrical and Computer Engineering, 2018. ET:6844.$Advisor : Dr. Fadi Karameh, Associate Professor, Electrical and Computer Engineering ; Committee members : Dr. Ibrahim Abou Faycal, Professor, Electrical and Computer Engineering ; Dr. Sami Karaki, Professor, Electrical and Computer Engineering ; Dr. Louay Bazzi, Professor, Electrical and Computer Engineering. |
dc.description |
Includes bibliographical references (leaves 59-60) |
dc.description.abstract |
About one third of epilepsy patients do not respond well to drug treatment. For part of this population, surgical intervention is a promising solution whereby the brain tissue causing seizure initiation is removed (the seizure onset zone or SoZ). Here, several clinical tests are normally conducted to detect and highlight the SoZ, including scalp EEG, MRI, SPECT, PET. Pre-surgical intracranial EEG (IEEG) is collected from multi-electrode arrays placed on the cortical surface to improve SoZ detection. Among the various multivariate techniques used to study the collected IEEG, conditional transfer entropy is very effective in finding causal relationships between the signals in recorded channels due to its generality and exhaustiveness. It is, however computationally very expensive so that using traditional CPUs is impractical. In this thesis, we used GPUs where thousands of cores could run in parallel to implement multi-variate CTE studies. Moreover, we focused on code optimization where the same functions could be implemented in untraditional way so that faster execution is achieved. Part of the code optimization is memory management where different types of memories with different speeds are available on GPUs. We reduced time needed from few days to few hours, thereby rendering the ability of applying CTE to IEEG data more readily attainable for research in SoZ prediction as well as other studies of high-dimensional causal interaction. |
dc.format.extent |
1 online resource (x, 60 leaves) : illustrations (some color) |
dc.language.iso |
eng |
dc.subject.classification |
ET:006844 |
dc.subject.lcsh |
Entropy (Information theory)$Graphics processing units.$Epilepsy. |
dc.title |
Transfer entropy calculations using GPUS for determining epilepsy focus - |
dc.type |
Thesis |
dc.contributor.department |
Maroun Semaan Faculty of Engineering and Architecture.$Department of Electrical and Computer Engineering, |
dc.contributor.institution |
American University of Beirut. |