Simple, Transparent, and Flexible Automated Quality Assessment Procedures for Ambulatory Electrodermal Activity Data

Electrodermal activity (EDA) is a noninvasive measure of sympathetic activation often used to study emotions, decision making, and health. The use of "ambulatory" EDA in everyday life presents novel challenges-frequent artifacts and long recordings-with inconsistent methods available for efficiently and accurately assessing data quality. We developed and validated a simple, transparent, flexible, and automated quality assessment procedure for ambulatory EDA data.

A total of 20 individuals with autism (5 females, 5-13 years) provided a combined 181 h of EDA data in their home using the Affectiva Q Sensor across 8 weeks. Our procedure identified invalid data using four rules: First, EDA out of range; second, EDA changes too quickly; third, temperature suggests the sensor is not being worn; and fourth, transitional data surrounding segments identified as invalid via the preceding rules. We identified invalid portions of a pseudorandom subset of our data (32.8 h, 18%) using our automated procedure and independent visual inspection by five EDA experts.

Our automated procedure identified 420 min (21%) of invalid data. The five experts agreed strongly with each other (agreement: 98%, Cohen's κ: 0.87) and, thus, were averaged into a "consensus" rating. Our procedure exhibited excellent agreement with the consensus rating (sensitivity: 91%, specificity: 99%, accuracy: 92%, κ: 0.739 [95% CI = 0.738, 0.740]).

We developed a simple, transparent, flexible, and automated quality assessment procedure for ambulatory EDA data.

Our procedure can be used beyond this study to enhance efficiency, transparency, and reproducibility of EDA analyses, with free software available at http://www.cbslab.org/EDAQA.