Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Ivo S. G. Brites; Lidia M. Silva; Jorge Luis Victória Barbosa; Sandro J. Rigo; Sergio Duarte Correia; Valderi Reis Quietinho Leithardt

doi:10.20944/preprints202110.0161.v1

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preprints.org > engineering > control and systems engineering > doi: 10.20944/preprints202110.0161.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Ivo S. G. Brites

^* ,

Lidia M. Silva

Jorge Luis Victória Barbosa

Sandro J. Rigo

Sergio Duarte Correia

^* ,

Valderi Reis Quietinho Leithardt

Version 1 : Received: 8 October 2021 / Approved: 11 October 2021 / Online: 11 October 2021 (14:03:09 CEST)

A peer-reviewed article of this Preprint also exists.

Brites, I.S.G.; da Silva, L.M.; Barbosa, J.L.V.; Rigo, S.J.; Correia, S.D.; Leithardt, V.R.Q. Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. Informatics 2021, 8, 73. Brites, I.S.G.; da Silva, L.M.; Barbosa, J.L.V.; Rigo, S.J.; Correia, S.D.; Leithardt, V.R.Q. Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review. Informatics 2021, 8, 73.

Abstract

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

Keywords

Machine Learning; IoT; Ubiquitous Computing; Wearables; Cardiovascular Diseases.

Subject

Engineering, Control and Systems Engineering

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.

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