Classification of anxiety based on EDA and HR

Abstract

This work presents anxiety classification using physiological data, namely, EDA (eletrodermal activity) and HR (heart rate), collected with a sensing wrist-wearable device during a neutral baseline state condition. For this purpose, the WESAD public available dataset was used. The baseline condition was collected for around 20 min on 15 participants. Afterwards, to assess anxiety scores, the shortened 6-item STAI was filled by the participants. Using train and test sets with 70% and 30% of data, respectively, the proposed ensemble of 100 bagged classification trees obtained an overall accuracy of 95.7%. This, along with the high precision and recall obtained, reveal the good performance of the proposed classifier and support the ability of anxiety score classification using physiological data. Such a classification task can be integrated in a mobile application presenting coping strategies to deal and manage anxiety.