Evidence map›Paper›PMID 33732168›Full record

ArticleFrontiers in physiology2021

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

Abstract read
In one paragraph

Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  13. Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yu-Min ChungDepartment of Mathematics and Statistics, University of North Carolina at Greensboro, Greensboro, NC, United States.
Chuan-Shen HuDepartment of Mathematics, National Taiwan Normal University, Taipei, Taiwan.
Yu-Lun LoDepartment of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University, School of Medicine, Taipei, Taiwan.
Hau-Tieng WuDepartment of Mathematics and Department of Statistical Science, Duke University, Durham, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

heart rate variabilitypersistence diagrampersistence statisticspersistent homologysleep stage

Identifiers

PMID33732168
PMCPMC7959762

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.