{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T21:53:13Z","timestamp":1781905993145,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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 structures in a broad range of data analytics problems including the study of cardiovascular signals. Here, we aim 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 in the form of a literature or narrative review. 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 shows 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.<\/jats:p>","DOI":"10.3390\/e26010067","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T07:47:16Z","timestamp":1705045636000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Topological Data Analysis in Cardiovascular Signals: An Overview"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1872-1397","authenticated-orcid":false,"given":"Enrique","family":"Hern\u00e1ndez-Lemus","sequence":"first","affiliation":[{"name":"Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico"},{"name":"Center for Complexity Sciences, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Mexico City 04510, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8142-7027","authenticated-orcid":false,"given":"Pedro","family":"Miramontes","sequence":"additional","affiliation":[{"name":"Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico"},{"name":"Department of Mathematics, Sciences School, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Mexico City 04510, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2876-8500","authenticated-orcid":false,"given":"Mireya","family":"Mart\u00ednez-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Immunology, National Institute of Cardiology, Mexico City 14080, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s11936-019-0728-1","article-title":"Artificial intelligence in cardiovascular medicine","volume":"21","author":"Seetharam","year":"2019","journal-title":"Curr. 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