- Rui João Pinto12,
- Pedro Miguel Silva12,
- Rui Pedro Duarte12,
- Francisco Alexandre Marinho12,
- António Jorge Gouveia12,
- Norberto Jorge Gonçalves12,
- Paulo Jorge Coelho13,14,
- Eftim Zdravevski15,
- Petre Lameski15,
- Nuno M. Garcia16,17 &
- …
- Ivan Miguel Pires16,18
Part of the book series:Lecture Notes in Computer Science ((LNBI,volume 13919))
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Abstract
An electrocardiogram (ECG) is a simple test that checks the heart’s rhythm and electrical activity and can be used by specialists to detect anomalies that could be linked to diseases. This paper intends to describe the results of several artificial intelligence methods created to automate identifying and classifying potential cardiovascular diseases through electrocardiogram signals. The ECG data utilized was collected from a total of 46 individuals (24 females, aged 26 to 90, and 22 males, aged 19 to 88) using a BITalino (r)evolution device and the OpenSignals (r)evolution software. Each ECG recording contains around 60 s, where, during 30 s, the individuals were in a standing position and seated down during the remaining 30 s. The best performance in identifying cardiovascular diseases with ECG data was achieved with the Naive Bays classifier, reporting an accuracy of 81.36%, a precision of 26.48%, a recall of 28.16%, and an F1-Score of 27.29%.
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Acknowledgments
This work is funded by FCT/MEC through national funds and co-funded by FEDER – PT2020 partnership agreement under the projectUIDB/50008/2020.
This work is also funded by FCT/MEC through national funds and co-funded by FEDER – PT2020 partnership agreement under the projectUIDB/00308/2020.
This article is based upon work from COST Action CA19101 - Determinants of Physical Activities in Settings (DE-PASS), supported by COST (European Cooperation in Science and Technology). More information onwww.cost.eu.
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Authors and Affiliations
Escola de Ciências E Tecnologia, University of Trás-Os-Montes E Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal
Rui João Pinto, Pedro Miguel Silva, Rui Pedro Duarte, Francisco Alexandre Marinho, António Jorge Gouveia & Norberto Jorge Gonçalves
Polytechnic of Leiria, Leiria, Portugal
Paulo Jorge Coelho
Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra), Coimbra, Portugal
Paulo Jorge Coelho
Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000, Skopje, Macedonia
Eftim Zdravevski & Petre Lameski
Instituto de Telecomunicações, 6201-001, Covilhã, Portugal
Nuno M. Garcia & Ivan Miguel Pires
Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisbon, Portugal
Nuno M. Garcia
Polytechnic Institute of Santarém, Santarém, Portugal
Ivan Miguel Pires
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University of Granada, Granada, Spain
Ignacio Rojas
University of Granada, Granada, Spain
Olga Valenzuela
University of Granada, Granada, Spain
Fernando Rojas Ruiz
University of Granada, Granada, Spain
Luis Javier Herrera
University of Granada, Granada, Spain
Francisco Ortuño
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Pinto, R.J.et al. (2023). Preliminary Study on the Identification of Diseases by Electrocardiography Sensors’ Data. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_23
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