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dc.contributor.authorWang, Yuan
dc.contributor.authorOmbao, Hernando
dc.contributor.authorChung, Moo K.
dc.date.accessioned2019-07-11T06:07:49Z
dc.date.available2019-07-11T06:07:49Z
dc.date.issued2019-05
dc.identifier.citationWang, Y., Ombao, H., & Chung, M. K. (2019). Statistical Persistent Homology of Brain Signals. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2019.8682978
dc.identifier.doi10.1109/ICASSP.2019.8682978
dc.identifier.urihttp://hdl.handle.net/10754/655972
dc.description.abstractTopological data analysis (TDA) extracts hidden topological features in signals that cannot be easily decoded by standard signal processing tools. A key TDA method is persistent homology (PH), which summarizes the changes of connected components in a signal through a multiscale descriptor such as the persistent landscape (PL). A recent development indicates that statistical inference on PLs of scalp electroencephalographic (EEG) signals produces markers for localizing seizure foci. However, a key obstacle of applying PH to large-scale clinical EEGs is the ambiguity of performing statistical inference. To address this problem, we develop a unified permutation-based inference framework for testing statistical indifference in PLs of EEG signals before and during an epileptic seizure. Compared with the standard permutation test, the proposed framework is shown to have more robustness when signals undergo non-topological changes and more sensitivity when topological changes occur. Furthermore, the proposed new method drastically improves the average computation time by 15000 folds.
dc.description.sponsorshipSupport for Moo K. Chung was provided by the NIH Brain Initiative grant EB022856
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8682978/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8682978
dc.rightsArchived with thanks to (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectEEG
dc.subjectpersistent homology
dc.subjectpersistence landscape
dc.subjectexact permutation test
dc.titleStatistical Persistent Homology of Brain Signals
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStatistics
dc.contributor.departmentStatistics Program
dc.conference.date12-17 May 2019
dc.conference.nameICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
dc.conference.locationBrighton, United Kingdom
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of South Carolina, U.S.A.
dc.contributor.institutionUniversity of Wisconsin-Madison, U.S.A
kaust.personOmbao, Hernando


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