Hernández-Lemus, E.; Miramontes, P.; Martínez-García, M. Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy2024, 26, 67.
Hernández-Lemus, E.; Miramontes, P.; Martínez-García, M. Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy 2024, 26, 67.
Hernández-Lemus, E.; Miramontes, P.; Martínez-García, M. Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy2024, 26, 67.
Hernández-Lemus, E.; Miramontes, P.; Martínez-García, M. Topological Data Analysis in Cardiovascular Signals: An Overview. Entropy 2024, 26, 67.
Abstract
Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets, based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structure in a broad range of data analytics problems including the study of cardiovascular signals. This review aims to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA has shown great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
