Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
Hanna Vitaliyivna Denysyuk,
Rui João Pinto,
Pedro Miguel Silva,
Rui Pedro Duarte,
Francisco Alexandre Marinho,
Luís Pimenta,
António Jorge Gouveia,
Norberto Jorge Gonçalves,
Paulo Jorge Coelho,
Eftim Zdravevski,
Petre Lameski,
Valderi Leithardt,
Nuno M. Garcia,
Ivan Miguel Pires
Affiliations
Hanna Vitaliyivna Denysyuk
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
Rui João Pinto
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Pedro Miguel Silva
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Rui Pedro Duarte
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Francisco Alexandre Marinho
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Luís Pimenta
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
António Jorge Gouveia
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Norberto Jorge Gonçalves
Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
Paulo Jorge Coelho
Polytechnic of Leiria, Leiria, Portugal; Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
Eftim Zdravevski
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
Petre Lameski
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, Macedonia
Valderi Leithardt
VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal; COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisboa, Portugal
Nuno M. Garcia
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
Ivan Miguel Pires
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal; Corresponding author.
The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
