Preliminary Study on Gender Identification by Electrocardiography Data

Eduarda Sofia Bastos; Rui Pedro Duarte; Francisco Alexandre Marinho; Luís Pimenta; António Jorge Gouveia; Norberto Jorge Gonçalves; Paulo Jorge Coelho; Eftim Zdravevski; Petre Lameski; Nuno M. Garcia; Ivan Miguel Pires

IoT Technologies for HealthCare. 9th EAI International Conference, HealthyIoT 2022, Braga, Portugal, November 16-18, 2022, Proceedings

Research Article

Preliminary Study on Gender Identification by Electrocardiography Data

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-031-28663-6_4,
    author={Eduarda Sofia Bastos and Rui Pedro Duarte and Francisco Alexandre Marinho and Lu\^{\i}s Pimenta and Ant\^{o}nio Jorge Gouveia and Norberto Jorge Gon\`{e}alves and Paulo Jorge Coelho and Eftim Zdravevski and Petre Lameski and Nuno M. Garcia and Ivan Miguel Pires},
    title={Preliminary Study on Gender Identification by Electrocardiography Data},
    proceedings={IoT Technologies for HealthCare. 9th EAI International Conference, HealthyIoT 2022, Braga, Portugal, November 16-18, 2022, Proceedings},
    proceedings_a={HEALTHYIOT},
    year={2023},
    month={3},
    keywords={ECG Gender identification Artificial intelligence Sensors},
    doi={10.1007/978-3-031-28663-6_4}
}

Eduarda Sofia Bastos
Rui Pedro Duarte
Francisco Alexandre Marinho
Luís Pimenta
António Jorge Gouveia
Norberto Jorge Gonçalves
Paulo Jorge Coelho
Eftim Zdravevski
Petre Lameski
Nuno M. Garcia
Ivan Miguel Pires
Year: 2023
Preliminary Study on Gender Identification by Electrocardiography Data
HEALTHYIOT
Springer
DOI: 10.1007/978-3-031-28663-6_4

Eduarda Sofia Bastos¹, Rui Pedro Duarte¹, Francisco Alexandre Marinho¹, Luís Pimenta¹, António Jorge Gouveia¹, Norberto Jorge Gonçalves¹^,*, Paulo Jorge Coelho², Eftim Zdravevski³, Petre Lameski³, Nuno M. Garcia⁴, Ivan Miguel Pires⁴

1: Escola de Ciências e Tecnologia, University of Trás-os-Montes e Alto Douro, Quinta de Prados
2: Polytechnic of Leiria
3: Faculty of Computer Science and Engineering, University Ss Cyril and Methodius
4: Instituto de Telecomunicações, Universidade da Beira Interior

*Contact email: njg@utad.pt

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.

Keywords: ECG Gender identification Artificial intelligence Sensors

Published: 2023-03-16
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-031-28663-6_4

Preliminary Study on Gender Identification by Electrocardiography Data

Abstract

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