Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 accuracy 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.

Original languageEnglish
Title of host publicationBIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
EditorsFelix Putze, Ana Fred, Hugo Gamboa
PublisherSciTePress
Pages42-51
Number of pages10
ISBN (Electronic)9789897583537
Publication statusPublished - 1 Jan 2019
Event12th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019 - Prague, Czech Republic
Duration: 22 Feb 201924 Feb 2019

Conference

Conference12th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
CountryCzech Republic
CityPrague
Period22/02/1924/02/19

Fingerprint

Neurology
Photoplethysmography
Monitoring
Spectrum analysis
Learning systems

Keywords

  • Autonomous nervous system
  • Biosignals
  • Classification
  • Electrodermal activity
  • Heart rate variability
  • Machine-learning
  • Photoplethysmography
  • Wearable device

Cite this

Lima, R., Osório, D., & Gamboa, H. (2019). Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome. In F. Putze, A. Fred, & H. Gamboa (Eds.), BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019 (pp. 42-51). SciTePress.
Lima, Rodrigo ; Osório, Daniel ; Gamboa, Hugo. / Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome. BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. editor / Felix Putze ; Ana Fred ; Hugo Gamboa. SciTePress, 2019. pp. 42-51
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title = "Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome",
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 accuracy 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.",
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Lima, R, Osório, D & Gamboa, H 2019, Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome. in F Putze, A Fred & H Gamboa (eds), BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. SciTePress, pp. 42-51, 12th International Conference on Bio-Inspired Systems and Signal Processing, BIOSIGNALS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019, Prague, Czech Republic, 22/02/19.

Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome. / Lima, Rodrigo; Osório, Daniel; Gamboa, Hugo.

BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. ed. / Felix Putze; Ana Fred; Hugo Gamboa. SciTePress, 2019. p. 42-51.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome

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AU - Osório, Daniel

AU - Gamboa, Hugo

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 accuracy 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.

AB - 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 accuracy 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.

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M3 - Conference contribution

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BT - BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019

A2 - Putze, Felix

A2 - Fred, Ana

A2 - Gamboa, Hugo

PB - SciTePress

ER -

Lima R, Osório D, Gamboa H. Heart rate variability and electrodermal activity in mental stress aloud: Predicting the outcome. In Putze F, Fred A, Gamboa H, editors, BIOSIGNALS 2019 - 12th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019. SciTePress. 2019. p. 42-51