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

arXiv: https://arxiv.org/abs/2104.13479 · HTML: https://mlsanity.com/paper/2104.13479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [(Quasi)Periodicity Quantification in Video Data, Using Topology](https://mlsanity.com/paper/1704.08382/) (2017)
- [A Short Survey of Topological Data Analysis in Time Series and Systems Analysis](https://mlsanity.com/paper/1809.10745/) (2018)

## Abstract (arXiv)

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.