A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

Yu Min Chung*, Chuan Shen Hu, Yu Lun Lo, Hau Tieng Wu

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

25 Scopus citations

Abstract

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

Original languageEnglish
Article number637684
JournalFrontiers in Physiology
Volume12
DOIs
StatePublished - 01 03 2021

Bibliographical note

Publisher Copyright:
© Copyright © 2021 Chung, Hu, Lo and Wu.

Keywords

  • heart rate variability
  • persistence diagram
  • persistence statistics
  • persistent homology
  • sleep stage

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