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Topological Data Analysis in Cardiovascular Signals: An Overview.

Enrique Hernández-Lemus1,2, Pedro Miramontes1,3, Mireya Martínez-García4

  • 1Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico.

Entropy (Basel, Switzerland)
|January 22, 2024
PubMed
Summary
This summary is machine-generated.

Topological data analysis (TDA) offers novel insights into complex cardiovascular signals. This review explores TDA

Keywords:
alegbraic topologycardiovascular signalsmapper algorithmpersistent homologytopological data analysis

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Area of Science:

  • Cardiovascular Research
  • Data Science
  • Applied Mathematics

Background:

  • Cardiovascular diseases (CVDs) represent a significant global health burden.
  • Analyzing complex cardiovascular signals is crucial for understanding and managing CVDs.
  • Traditional methods may struggle to capture intricate patterns in cardiovascular data.

Purpose of the Study:

  • To provide a comprehensive overview of Topological Data Analysis (TDA) applications in cardiovascular signal analysis.
  • To explore TDA's potential in enhancing the understanding and treatment of cardiovascular diseases.
  • To review current literature on TDA for cardiovascular signals.

Main Methods:

  • Introduction to core TDA concepts: persistent homology, Mapper, and multidimensional scaling.
  • Review of TDA applications across various cardiovascular signals (ECG, PPG, arterial stiffness).
  • Discussion of TDA's role in diagnosis, prognosis, and clinical practice.

Main Results:

  • TDA effectively disentangles complex data structures in cardiovascular signals.
  • TDA techniques like persistent homology and Mapper reveal non-trivial patterns.
  • Applications span electrocardiography, photoplethysmography, and arterial stiffness analysis.

Conclusions:

  • Topological Data Analysis shows significant promise for cardiovascular signal analysis.
  • TDA can offer deeper insights into cardiovascular diseases and their management.
  • Future directions include broader clinical integration and methodological advancements.