Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea

Topological data analysis (TDA) is an emerging technique for biological signal processing. TDA leverages the invariant topological features of signals in a metric space for robust analysis of signals even in the presence of noise. In this paper, we leverage TDA on brain connectivity networks derived...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Manjunath, Shashank, Perea, Jose A, Sathyanarayana, Aarti
Format: Article
Language:English
Subjects:
Brain
Data analysis
Electroencephalography
Metric space
Signal processing
Topology
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Topological data analysis (TDA) is an emerging technique for biological signal processing. TDA leverages the invariant topological features of signals in a metric space for robust analysis of signals even in the presence of noise. In this paper, we leverage TDA on brain connectivity networks derived from electroencephalogram (EEG) signals to identify statistical differences between pediatric patients with obstructive sleep apnea (OSA) and pediatric patients without OSA. We leverage a large corpus of data, and show that TDA enables us to see a statistical difference between the brain dynamics of the two groups.
ISSN:2331-8422
DOI:10.48550/arxiv.2304.14853