# Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea

arXiv: https://arxiv.org/abs/2304.14853 · HTML: https://mlsanity.com/paper/2304.14853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals](https://mlsanity.com/paper/2405.09566/) (2024)
- [Two-Stage Hierarchical and Explainable Feature Selection Framework for Dimensionality Reduction in Sleep Staging](https://mlsanity.com/paper/2409.00565/) (2024)

## Abstract (arXiv)

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.