Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea
Algebraic Topology
2024-01-11 v1 Computational Geometry
Abstract
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.
Keywords
Cite
@article{arxiv.2304.14853,
title = {Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea},
author = {Shashank Manjunath and Jose A. Perea and Aarti Sathyanarayana},
journal= {arXiv preprint arXiv:2304.14853},
year = {2024}
}