On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals

Frank R. Ihmig*, H. Antonio Gogeascoechea, Frank Neurohr-Parakenings, Sarah K. Schäfer, Johanna Lass-Hennemann, Tanja Michael

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere0231517
JournalPLoS ONE
Volume15
Issue number6
DOIs
Publication statusPublished - Jun 2020

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