Abstract
Medical teams can use an electrocardiogram (ECG) as a quick test to examine the electrical activity and rhythm of the heart to look for irregularities that may be indicative of diseases. This work aims to summarize the outcomes of several artificial intelligence techniques developed to identify ECG data by gender automatically. The analysis and processing of ECG data were collected from 219 individuals (112 males, 106 females, and one other) aged between 12 and 92 years in different geographical regions, located mainly in the municipalities of the center of Portugal. These data allowed to discretize gender by the analysis of ECG data during the experiment performed and were acquired with the BITalino (r)evolution device, connected to a personal computer, using the OpenSignals (r)evolution software. The dataset describes the acquisition conditions, the individual’s characteristics, and the sensors used as the data acquired from the ECG sensor.
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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 (Este trabalho é financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/50008/2020).
This article is based upon work from COST Action CA19136 - International Interdisciplinary Network on Smart Healthy Age-friendly Environments (NET4AGE-FRIENDLY), supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu.
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Bastos, E.S. et al. (2023). Preliminary Study on Gender Identification by Electrocardiography Data. In: Spinsante, S., Iadarola, G., Paglialonga, A., Tramarin, F. (eds) IoT Technologies for HealthCare. HealthyIoT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-031-28663-6_4
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