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Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method\u2019s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.<\/jats:p>","DOI":"10.1093\/comnet\/cnad030","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T16:52:21Z","timestamp":1693932741000},"source":"Crossref","is-referenced-by-count":2,"title":["Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4469-5049","authenticated-orcid":false,"given":"Aruane M","family":"Pineda","sequence":"first","affiliation":[{"name":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4708-1330","authenticated-orcid":false,"given":"Caroline L","family":"Alves","sequence":"additional","affiliation":[{"name":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP), S\u00e3o Paulo, 13566-590, Brazil and Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"M\u00f6ckel","sequence":"additional","affiliation":[{"name":"Aschaffenburg University of Applied Sciences, Laboratory for Hybrid Modeling , Aschaffenburg 63743, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thaise G L de O","family":"Toutain","sequence":"additional","affiliation":[{"name":"Health Sciences Institute(HSI), Federal University of Bahia (UFBA) , Bahia, 40110-909, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joel Augusto","family":"Moura Porto","sequence":"additional","affiliation":[{"name":"Institute of Physics of S\u00e3o Carlos (IFSC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco A","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Institute of Mathematical and Computer Sciences (ICMC), University of S\u00e3o Paulo (USP) , S\u00e3o Paulo, 13566-590, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,9,5]]},"reference":[{"key":"2023102015242577200_cnad030-B1","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.bbe.2021.02.007","article-title":"A comprehensive survey on low-cost ECG acquisition systems: advances on design specifications, challenges and future direction","volume":"41","author":"Faruk","year":"2021","journal-title":"Biocybernet. 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