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 project UIDB/50008/2020.
This work is also funded by FCT/MEC through national funds and co-funded by FEDER – PT2020 partnership agreement under the project UIDB/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 on www.cost.eu.
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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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