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dc.contributor.authorWang, Yuan
dc.contributor.authorOmbao, Hernando
dc.contributor.authorChung, Moo K.
dc.date.accessioned2019-03-11T07:13:18Z
dc.date.available2019-03-11T07:13:18Z
dc.date.issued2018-09-11
dc.identifier.citationWang Y, Ombao H, Chung MK (2018) Topological data analysis of single-trial electroencephalographic signals. The Annals of Applied Statistics 12: 1506–1534. Available: http://dx.doi.org/10.1214/17-AOAS1119.
dc.identifier.issn1932-6157
dc.identifier.doi10.1214/17-AOAS1119
dc.identifier.urihttp://hdl.handle.net/10754/631519
dc.description.abstractEpilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Statistical analysis of neuro-physiological recordings, such as electroencephalography (EEG), facilitates the understanding of epileptic seizures. Standard statistical methods typically analyze amplitude and frequency information in EEG signals. In the current study, we propose a topological data analysis (TDA) framework to analyze single-trial EEG signals. The framework denoises signals with a weighted Fourier series (WFS), and tests for differences between the topological features—persistence landscapes (PLs) of denoised signals through resampling in the frequency domain. Simulation studies show that the test is robust for topologically similar signals while bearing sensitivity to topological tearing in signals. In an application to single-trial epileptic EEG signals, EEG signals in the diagnosed seizure origin and its symmetric site are found to have similar PLs before and during a seizure attack, in contrast to signals at other sites showing significant statistical difference in the PLs of the two phases.
dc.description.sponsorshipSupported in part by NIH Brain Initiative Grant EB022856 Supported in part by NSF DMS, NSF SES and the KAUST Baseline Research Fund.
dc.publisherInstitute of Mathematical Statistics
dc.relation.urlhttps://projecteuclid.org/euclid.aoas/1536652963
dc.rightsArchived with thanks to The Annals of Applied Statistics
dc.subjectElectroencephalogram
dc.subjectEpilepsy
dc.subjectPersistence landscape
dc.subjectPersistent homology
dc.subjectWeighted fourier series
dc.titleTopological data analysis of single-trial electroencephalographic signals
dc.typeArticle
dc.contributor.departmentBiostatistics Group
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics Program
dc.identifier.journalThe Annals of Applied Statistics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI, 53705, , United States
dc.contributor.institutionDepartment of Statistics, University of California, Irvine, Irvine, CA, 92697, , United States
kaust.personOmbao, Hernando
refterms.dateFOA2019-09-11T00:00:00Z
dc.date.published-online2018-09-11
dc.date.published-print2018-09


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