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2021 | OriginalPaper | Chapter

Complex Networks to Differentiate Elderly and Young People

Authors : Aruane M. Pineda, Francisco A. Rodrigues

Published in: Information Management and Big Data

Publisher: Springer International Publishing

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Abstract

Cardiovascular disease (CVD) is a general term that describes different heart problems. There are several heart diseases, which still lead thousands of people to sudden death. Among them are high blood pressure, ischemia, variation in cardiac rhythms, and pericardial effusion. Studies about these diseases are usually made through the analysis of electrocardiogram (ECG) signals, which presents valuable information on the development of the heart’s status. Recent papers have posited the creation of quantile graphs (QG) using data from ECG. In this method, based on transition probabilities, these quantile graphs are a result of a time series mapped into a network. This so-called QG method can be employed to differentiate between young and elderly patients using their ECG signals. The primary goal of our paper is to show how variations in ECG signals are mirrored in the respective QGs’ topology. Our analyses were centered on three metrics: mean jump length, betweenness centrality and clustering coefficient. The results indicate that the QG method is a reliable tool for differentiating ECG exams regarding the age of the patients.

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Title: Complex Networks to Differentiate Elderly and Young People
Authors: Aruane M. Pineda
Francisco A. Rodrigues
Publisher: Springer International Publishing
Book: Information Management and Big Data
Print ISBN: 978-3-030-76227-8

Electronic ISBN: 978-3-030-76228-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-76228-5_31

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