Inferring Spectral and Spatiotemporal Dependencies from Data and its Application to Epilepsy

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dc.contributor.advisorAazhang, Behnaam
dc.creatorMalladi, Rakesh
dc.date.accessioned2017-08-01T17:58:44Z
dc.date.available2017-08-01T17:58:44Z
dc.date.created2017-05
dc.date.issued2017-03-28
dc.date.submittedMay 2017
dc.date.updated2017-08-01T17:58:44Z
dc.description.abstractA fundamental problem in many science and engineering disciplines is inferring the characteristics of a physical or biological system from the dependencies in data recorded from the system. The dependencies in data, particularly in case of signals recorded from brain, are commonly believed to be nonlinear and the underlying model is often unknown. This thesis focusses on developing novel information-theoretic approaches to detect and quantify spectral and spatiotemporal dependencies from data in a data-driven manner and applies them to electrocorticographic (ECoG) recordings from epilepsy patients to unravel epileptic brain networks. Frequency components in a signal or between two signals, not necessarily at the same frequency, are spectrally dependent if they are not statistically independent. Two signals are temporally dependent if the past measurements at one decrease the uncertainty in predicting the other. First, we define a novel metric, mutual information in frequency, to detect spectral dependency and quantify it using a data-driven estimator. We then develop a data-driven estimator of mutual information between dependent data using mutual information in frequency. Next, we develop a model-based and a data-driven estimator of directed information to detect and quantify the temporal dependencies in data. Finally, we apply the proposed metrics to ECoG recordings from epilepsy patients to identify seizure onset zone (SOZ), to learn the spatiotemporal characteristics of seizures and to infer the cross-frequency coupling in SOZ. We observe that seizure onset zone drives the rest of brain to a seizure during pre-seizure and seizure periods, while it acts as a sink during post-seizure periods. In addition, high frequency coupling increases during seizures within an ECoG channel and between channels in the same anatomical region in SOZ, but not between different regions in SOZ. This suggests different anatomical regions in the SOZ are independently driving the seizure activity and any treatment should potentially target these regions simultaneously. Going forward, the dependencies unraveled by the proposed metrics should be further analyzed to optimize the parameters of closed-loop electrical stimulation based treatments for epilepsy.
dc.format.mimetypeapplication/pdf
dc.identifier.citationMalladi, Rakesh. "Inferring Spectral and Spatiotemporal Dependencies from Data and its Application to Epilepsy." (2017) Diss., Rice University. <a href="https://hdl.handle.net/1911/96081">https://hdl.handle.net/1911/96081</a>.
dc.identifier.urihttps://hdl.handle.net/1911/96081
dc.language.isoeng
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.
dc.subjectEpilepsy
dc.subjectdirected information
dc.subjectseizure onset zone
dc.subjectECoG
dc.subjectcausal connectivity
dc.subjectMutual information
dc.subjectfrequency
dc.subjectdependent data
dc.subjectmutual information in frequency
dc.subjectCramér’s spectral representation
dc.subjectcross-frequency coupling
dc.titleInferring Spectral and Spatiotemporal Dependencies from Data and its Application to Epilepsy
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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