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...

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Bibliographic Details
Main Authors: Manjunath, Shashank, Perea, Jose A., Sathyanarayana, Aarti
Format: Conference Proceeding
Language:English
Subjects:
Biology
Brain
Child
Data analysis
Electric potential
Electroencephalography
Electroencephalography - methods
Extraterrestrial measurements
Humans
Polysomnography - methods
Signal processing
Sleep apnea
Sleep Apnea, Obstructive - diagnosis
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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.Clinical relevance- This establishes the potential of topological data analysis as a tool to identify obstructive sleep apnea without requiring a full polysomnogram study, and provides an initial investigation towards easier and more scalable obstructive sleep apnea diagnosis.
ISSN:2694-0604
DOI:10.1109/EMBC40787.2023.10340674