Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology

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. Crown Copyright (c) 2021 Published by Elsevier Inc. All rights reserved.

Automated summary

This article addresses the diagnosis issue of sleep apnea and investigates it using clustering analysis and topological methods.