Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology
This work addresses the need for better phenotyping in pediatric sleep apnea diagnosis, though it appears incremental as it builds on existing clustering and topological techniques.
The study tackled the problem of ineffective traditional diagnosis of pediatric sleep apnea by phenotyping patients through clustering analysis of airflow time series, resulting in the development of novel methods using fuzzy clustering and persistent homology to analyze signal features and periodicity.
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.