Application of Embedded Dynamic Mode Decomposition on Epileptic Data for Seizure Prediction

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dc.contributor.advisorAazhang, Behnaamen_US
dc.creatorErfanian Taghvayi, Negaren_US
dc.date.accessioned2020-06-05T17:27:53Zen_US
dc.date.available2020-06-05T17:27:53Zen_US
dc.date.created2019-12en_US
dc.date.issued2020-05-27en_US
dc.date.submittedDecember 2019en_US
dc.date.updated2020-06-05T17:27:53Zen_US
dc.description.abstractThe underlying spatiotemporal mechanism that leads to the formation of seizures in the brain has been an interesting topic for decades. Different techniques have been proposed to extract the dynamics of epileptic recordings that are involved in seizure formation. Methods have been used to measure the synchrony between two or more epileptic recordings. These techniques are often model-based or suffer from poor time-frequency resolution. In this project, we introduce a data-driven toolbox called the Dynamic Mode Decomposition (DMD) with time-delay embedding to extract the underlying spatio-temporal dynamics of seizure formation. These techniques will enable us to focus on similarities among seizures in our attempt to better understand, detect, and predict seizures. The inferred information on the underlying dynamics of an epileptic system are essential in terms of improving the capability of stimulation-based treatments of epileptic patients.en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationErfanian Taghvayi, Negar. "Application of Embedded Dynamic Mode Decomposition on Epileptic Data for Seizure Prediction." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/108777">https://hdl.handle.net/1911/108777</a>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/108777en_US
dc.language.isoengen_US
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.subjectDynamic Mode Decompositionen_US
dc.subjectEpilepsyen_US
dc.subjectSeizure Predictionen_US
dc.subjectLinear Embeddingen_US
dc.titleApplication of Embedded Dynamic Mode Decomposition on Epileptic Data for Seizure Predictionen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
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