1 research outputs found
Autonoumous Nervous System biosignal processing via EDA and HRV from a wearable device
The assessment of changes in the autonomous nervous system (ANS) with certain diseases and
pathologies conditions, has been demonstrated to have important prognostic and diagnostic
value, so delineating the role of autonomous activity is important to prevent health diseases.
There are many approaches to directly measure the sympathetic and parasympathetic nervous
system, although, most of themare invasive and unable to provide continuous monitoring,
leading to inaccurate assessment of the autonomous nervous system.
Heart rate variability (HRV) and Electrodermal activity (EDA) are presented has noninvasive
methods to assess the ANS, by computing the spectral analysis of both HRV and EDA biosignals.
The combination of these signals is necessary to correctly measure the activity of the sympathetic
and parasympathetic system, due to the fact that frequency analysis of HRV only provides
the level of unbalance between these two systems, while EDA reflects only activity from the
sympathetic system. ANS biosignal processing via HRV and EDA from a wearable device was
studied in this thesis, in order to provide continuous monitoring. A wearable device is the ideal
solution, as HRV can be calculated with photoplethysmography signals from the wrist and EDA
from the fingers, providing wireless and continuous monitoring of the subjects.
The extraction of the HRV and EDA features, that describe the activity of the sympathetic
and parasympathetic system, were obtained by submitting the subjects to a mental arithmetic
stress test, and then compared to the baseline values, in order to verify changes in the autonomous
nervous system between the two situations.
The distinct response to stress for the subjects was then predicted usingmachine-learning
classification mechanisms, with the ability to predict how the subject will respond when submitted
to a situation of stress, using only time-domain features, instead of frequency-domain
features, which reduces the time needed to performthe classification