ArticlePloS one2021
Persistent homology as a new method of the assessment of heart rate variability.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Machine learning of time series data using persistent homology.Scientific reports · 2025Article
- Age-dependent patterns of cardiac complexity unveiled by topological data analysis of pediatric heart rate variability.PloS one · 2025Article
- Topological Data Analysis in Cardiovascular Signals: An Overview.Entropy (Basel, Switzerland) · 2024Review
- Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing.Sensors (Basel, Switzerland) · 2024Article
- Geometric characterization of the persistence of 1D maps.Journal of applied and computational topology · 2024Article
- The impact of war on the development and progression of arterial hypertension and cardiovascular disease: protocol of a prospective study among Ukrainian female refugees.Frontiers in cardiovascular medicine · 2023Review
- Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method.Frontiers in neuroscience · 2023Article
- ECG Classification Based on Wasserstein Scalar Curvature.Entropy (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.
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Registered trials
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.