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
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.
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