Multivariate principal oscillation pattern analysis of ICA sources during seizure

Tim Mullen, Gregory Worrell, Scott Makeig

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Mapping the dynamics of neural source processes critically involved in initiating and propagating seizure activity is important for effective epilepsy diagnosis, intervention, and treatment. Tracking time-varying shifts in the oscillation modes of an evolving seizure may be useful for both seizure onset detection as well as for improved non-surgical interventions such as microstimulation. In this report we apply a multivariate eigendecomposition method to analyze the time-varying principal oscillation patterns (POPs, or eigenmodes) of maximally-independent (ICA) sources of intracranial EEG data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. Our analysis of a subset of the most dynamically important eigenmodes reveals distinct shifts in characteristic frequency and damping time before, throughout, and following seizures providing insight into the dynamical structure of the system throughout seizure evolution.

Original languageEnglish (US)
Title of host publication2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages2921-2924
Number of pages4
DOIs
StatePublished - 2012
Event34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States
Duration: Aug 28 2012Sep 1 2012

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Other

Other34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
CountryUnited States
CitySan Diego, CA
Period8/28/129/1/12

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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