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Authors: Rodrigo Lima 1 ; Daniel Osório 2 and Hugo Gamboa 3

Affiliations: 1 Plux-Wireless Biosignals S.A, Avenida 5 de Outubro 70, 1050-59, Lisboa, Portugal, Department of Physics, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal ; 2 Plux-Wireless Biosignals S.A, Avenida 5 de Outubro 70, 1050-59, Lisboa, Portugal, Laboratório de Instrumentaç ão, Engenharia Biomédica e Física da Radiaç ão (LIBPhys-UNL), Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal ; 3 Plux-Wireless Biosignals S.A, Avenida 5 de Outubro 70, 1050-59, Lisboa, Portugal, Department of Physics, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal, Laboratório de Instrumentaç ão, Engenharia Biomédica e Física da Radiaç ão (LIBPhys-UNL), Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, Monte de Caparica, 2892-516, Caparica, Portugal

ISBN: 978-989-758-353-7

Keyword(s): Heart Rate Variability, Electrodermal Activity, Photoplethysmography, Autonomous Nervous System, Wearable Device, Biosignals, Machine-Learning, Classification.

Abstract: The assessment of changes in the autonomous nervous system (ANS), have important prognostic and diagnostic value, and can be used to assess stress levels. There are many approaches to directly measure the sympathetic and parasympathetic nervous system, although, most of them are invasive and unable to provide continuous monitoring. Heart rate variability (HRV) and Electrodermal activity (EDA) are noninvasive methods to assess the autonomous nervous system, by computing the spectral analysis of both HRV and EDA biosignals. In order to provide continuous monitoring, a wearable device is used, obtaining HRV features with photoplethysmography signals from the wrist and EDA from the fingers. The extraction of the HRV and EDA features, were obtained by submitting the subjects to a mental arithmetic stress test. The distinct response to stress was then classified using machine-learning techniques. The constructed models have the ability to predict how the subjects will respond, with an accur acy of approximately 80% in terms of HRV features in baseline and an accuracy of approximately 77% in terms of HRV and EDA simultaneous baseline features, when submitted to a situation of stress. (More)

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Paper citation in several formats:
Lima, R.; Osório, D. and Gamboa, H. (2019). Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-353-7, pages 42-51. DOI: 10.5220/0007355200420051

@conference{biosignals19,
author={Rodrigo Lima. and Daniel Osório. and Hugo Gamboa.},
title={Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2019},
pages={42-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007355200420051},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Heart Rate Variability and Electrodermal Activity in Mental Stress Aloud: Predicting the Outcome
SN - 978-989-758-353-7
AU - Lima, R.
AU - Osório, D.
AU - Gamboa, H.
PY - 2019
SP - 42
EP - 51
DO - 10.5220/0007355200420051

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