Abstract Number:
2916
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Mara Sherlin Talento (1), Jordan Richards (1), Marco Pinto-Orellana (2), Raphael Huser (3), Hernando Ombao (1)
Institutions:
(1) King Abdullah University of Science and Technology, N/A, (2) University of California - Irvine, California, (3) KAUST, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Spectral association plays a vital role in the study of functional brain connectivity, but traditional measures focus on linear spectral associations found in the bulk of the distribution. In certain studies, such as risk analysis, the interest shifts to connectivity in the tails of the distribution, as this reveals crucial information pertaining to extreme events, e.g., seizures. This motivates us to extend the notion of spectral association into the tail of the periodogram (given a specific frequency band) to study electroencephalogram (EEG) signals of seizure-prone neonates. Existing models are limited to tail of univariate periodogram or the tail associations of filtered series. In this study, we develop a non-stationary extremal dependence model for multivariate time series, that permits different dependence behaviour during different brain phases, i.e., high and low activity. This allows us to identify key tail-frequency connectivity at specific frequency bands that could trigger an outburst of energy, and we discuss these novel scientific findings alongside a comparison of the extremal behaviour of brain signals for ictal and non-ictal patients.
Keywords:
extreme value theory|spectral analysis|electroencephalogram (EEG)|spectral clustering|conditional extremes|periodogram
Sponsors:
Section on Statistics in Imaging
Tracks:
Imaging
Can this be considered for alternate subtype?
No
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand