TY - JOUR
T1 - On-line anxiety level detection from biosignals
T2 - Machine learning based on a randomized controlled trial with spider-fearful individuals
AU - Ihmig, Frank R.
AU - Antonio Gogeascoechea, H.
AU - Neurohr-Parakenings, Frank
AU - Schäfer, Sarah K.
AU - Lass-Hennemann, Johanna
AU - Michael, Tanja
N1 - Funding Information:
This research is funded by the German Federal Ministry of Education and Research through an applied research grant (contract numbers 13GW0158B and 13GW0158C) within the program "Medical technology solutions for the digital healthcare". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020 Ihmig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/6
Y1 - 2020/6
N2 - We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spiderfearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.
AB - We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spiderfearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia.
UR - http://www.scopus.com/inward/record.url?scp=85087017925&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0231517
DO - 10.1371/journal.pone.0231517
M3 - Article
C2 - 32574167
AN - SCOPUS:85087017925
SN - 1932-6203
VL - 15
JO - PLoS ONE
JF - PLoS ONE
IS - 6
M1 - e0231517
ER -