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Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation

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IoT Technologies for HealthCare (HealthyIoT 2022)

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Abstract

This paper presents a comparison of nine models for gender identification using nonstandard ECG signal. Methods: QRS features, QT interval, RR interval, HRV features and HR were extracted from three minutes of 40 ECG’s (from 24 female and 16 males) available at ALLab dataset and 108 ECG’s (from 52 female and 56 males) available at CYBHi dataset. Models were developed using Decision tree, SVM, kNN, Boosted tree, Bagged tree, Subspace kNN, Subspace Discriminant, two majority vote and verified by external validation. Results: The study presented achieved as best results an accuracy of 78% from Boosted tree and 85% from majority vote. Conclusion: The automatic detection of gender by ECG could be very important and improve the development of predictive systems for cardiovascular disease. These classifications are promising due to the use of nonstandard ECG and to the simplicity of extraction of features that potentiated the correct classification

This work was partially funded by FCT/MCTES through national funds and co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. This article is based on work from COST Action IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology).

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Authors and Affiliations

  1. Instituto de Telecomunicações, Covilhã, Portugal

    Henriques Zacarias, Virginie Felizardo, Leonice Souza-Pereira, André Pinho, Nuno Garcia & Nuno Pombo

  2. ALLab - Assisted Living Computing and Telecommunications Laboratory, Universidade da Beira Interior, Covilhã, Portugal

    Henriques Zacarias, Virginie Felizardo, Leonice Souza-Pereira, André Pinho, Susana Ramos, Mehran Pourvahab, Nuno Garcia & Nuno Pombo

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  1. Henriques Zacarias
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  2. Virginie Felizardo
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  3. Leonice Souza-Pereira
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  4. André Pinho
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  6. Mehran Pourvahab
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  7. Nuno Garcia
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  8. Nuno Pombo
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Correspondence to Henriques Zacarias .

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Editors and Affiliations

  1. Università Politecnica delle Marche, Ancona, Italy

    Susanna Spinsante

  2. Università Politecnica delle Marche, Ancona, Italy

    Grazia Iadarola

  3. CNR-Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Milan, Italy

    Alessia Paglialonga

  4. Università di Modena e Reggio Emilia, Modena, Italy

    Federico Tramarin

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Zacarias, H. et al. (2023). Gender Classification Using nonstandard ECG Signals - A Conceptual Framework of Implementation. 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_9

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  • DOI: https://doi.org/10.1007/978-3-031-28663-6_9

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  • Online ISBN: 978-3-031-28663-6

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