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dc.contributor.authorSundararajan, Raanju R
dc.contributor.authorFrostig, Ron
dc.contributor.authorOmbao, Hernando
dc.date.accessioned2020-12-14T13:44:15Z
dc.date.available2019-12-23T06:19:26Z
dc.date.available2020-12-14T13:44:15Z
dc.date.issued2020-12-06
dc.date.submitted2020-10-22
dc.identifier.citationSundararajan, R. R., Frostig, R., & Ombao, H. (2020). Modeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals. Entropy, 22(12), 1375. doi:10.3390/e22121375
dc.identifier.issn1099-4300
dc.identifier.pmid33279920
dc.identifier.doi10.3390/e22121375
dc.identifier.urihttp://hdl.handle.net/10754/660740
dc.description.abstractIn some applications, it is important to compare the stochastic properties of two multivariate time series that have unequal dimensions. A new method is proposed to compare the spread of spectral information in two multivariate stationary processes with different dimensions. To measure discrepancies, a frequency specific spectral ratio (FS-ratio) statistic is proposed and its asymptotic properties are derived. The FS-ratio is blind to the dimension of the stationary process and captures the proportion of spectral power in various frequency bands. Here we develop a technique to automatically identify frequency bands that carry significant spectral power. We apply our method to track changes in the complexity of a 32-channel local field potential (LFP) signal from a rat following an experimentally induced stroke. At every epoch (a distinct time segment from the duration of the experiment), the nonstationary LFP signal is decomposed into stationary and nonstationary latent sources and the complexity is analyzed through these latent stationary sources and their dimensions that can change across epochs. The analysis indicates that spectral information in the Beta frequency band (12-30 Hertz) demonstrated the greatest change in structure and complexity due to the stroke.
dc.description.sponsorshipThis work is support in part by KAUST, NIH NS066001, Leducq Foundation 15CVD02 and NIH MH115697.
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/1099-4300/22/12/1375
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleModeling Spectral Properties in Stationary Processes of Varying Dimensions with Applications to Brain Local Field Potential Signals.
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalEntropy (Basel, Switzerland)
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA.
dc.contributor.institutionSchool of Biological Sciences, University of California Irvine, Irvine, CA 92697, USA.
dc.identifier.volume22
dc.identifier.issue12
dc.identifier.pages1375
dc.identifier.arxivid1911.12295
kaust.personOmbao, Hernando
dc.date.accepted2020-12-03
refterms.dateFOA2019-12-23T06:19:48Z


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