JMIR Publications

ABSTRACT

Background:

Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals’ autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in the ambulatory setting.

Objective:

The primary goal of this manuscript is to describe and implement several methods for pre-processing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress.

Methods:

We use data from a study examining the effects of stressful tasks on adolescent mothers’ EDA. A biosensor band recorded EDA 4 times/sec. and was worn during an approximately 2 hr. assessment that included a 10-min. mother-child videotaped interaction. Initial processing included filtering noise and motion artifacts.

Results:

We constructed features of the EDA data, including the number of peaks and their amplitude as well as EDA reactivity, quantified as the rate at which adolescent mothers returned to baseline EDA following an EDA peak. Although the pattern of EDA varied substantially across individuals, various features of EDA may be computed for all individuals enabling within- and between individual analysis and comparisons.

Conclusions:

The algorithms we developed can be used to construct features for dry-electrode, ambulatory EDA that can be used by other researchers to study stress and anxiety.