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

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, particula...

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Bibliographic Details
Published in:Frontiers in physiology 2021-03, Vol.12, p.637684-637684
Main Authors: Chung, Yu-Min, Hu, Chuan-Shen, Lo, Yu-Lun, Wu, Hau-Tieng
Format: Article
Language:English
Subjects:
heart rate variability
persistence diagram
persistence statistics
persistent homology
Physiology
sleep stage
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Summary: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 . 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.
ISSN:1664-042X
1664-042X
DOI:10.3389/fphys.2021.637684