Sleep apnea is a disorder that has serious consequences for the pediatric population. There has been recent concern that traditional diagnosis of the disorder using the apnea-hypopnea index may be ineffective in capturing its multi-faceted outcomes. In this work, we take a first step in addressing this issue by phenotyping patients using a clustering analysis of airflow time series. This is approached in three ways: using feature-based fuzzy clustering in the time and frequency domains, and using persistent homology to study the signal from a topological perspective. The fuzzy clusters are analyzed in a novel manner using a Dirichlet regression analysis, while the topological approach leverages Takens embedding theorem to study the periodicity properties of the signals.
@article{arxiv.2104.13479,
title = {Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology},
author = {Prachi Loliencar and Giseon Heo},
journal= {arXiv preprint arXiv:2104.13479},
year = {2021}
}