Phenotyping OSA: a time series analysis using fuzzy clustering and persistent homology. (English) Zbl 1533.62074
Summary: 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.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 55N31 | Persistent homology and applications, topological data analysis |
| 62H86 | Multivariate analysis and fuzziness |
| 62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |
| 92C50 | Medical applications (general) |
Keywords:
clustering; persistent homology; time series; Dirichlet regression; phenotyping; sleep apneaReferences:
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