Validity of the EDA-Explorer as a means for artifact rejection and peak detection in electro dermal activity data-analysis

The recent increase in wearable technology and their application in scientific studies as well as in everyday life is calling for new ways of analyzing the data retrieved. In a psychological framework the combination of wearables with automated analysis software could make real-time biofeedback significantly more accessible to health care professionals. The Massachusetts Institute for Technology has developed the EDA-Explorer, a tool which promises to relieve the often unskilled professional from manually finding peaks and disturbances - so called artifacts - in the data sets of laboratory and ambulatory research. This study aims to validate this tool by comparing its output against the ‘golden standard’ for electrodermal activity (EDA) analysis. In a laboratory experiment, participants were asked to complete three tasks that would stress-test the Empatica E4, a measurement device for electrodermal activity. Peak detection and the artifact rejection were then performed by both the EDA-Explorer and Ledalab and Visual Inspection respectively. The EDA-Explorer’s Through-To-Peak method for identifying peaks could be shown to be accurate to the point of outperforming both Ledalab’s Through-To-Peak and Compulsive-Decomposition-Analysis method. Artifact rejection on the other hand is still in the early stages of development. The EDA-Explorer's binary and multiclass method should not be used in an ambulatory setting. All findings are discussed on the background of ambulatory studies, contrasting alternative tooling and starting a discussion on how to define artifacts for electrodermal activity data analysis. Additionally, this paper offers a short summary of issues discovered in using the Empatica E4 as a measurement device.