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Complex Networks to Differentiate Elderly and Young People

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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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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Authors and Affiliations

  1. Institute of Mathematical and Computer Sciences (ICMC), University of Sao Paulo (USP), Sao Carlos, Sao Paulo, Brazil

    Aruane M. Pineda & Francisco A. Rodrigues

Authors
  1. Aruane M. Pineda
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  2. Francisco A. Rodrigues
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Correspondence to Aruane M. Pineda .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Pineda, A.M., Rodrigues, F.A. (2021). Complex Networks to Differentiate Elderly and Young People. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_31

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