Applications of sparse recovery and dictionary learning to enhance analysis of ambulatory electrodermal activity data

Electrodermal Activity (EDA) − an index of sympathetic nervous system arousal − is one of the primary methods used in psychophysiology to assess the autonomic nervous system [1]. While many studies collect EDA data in short, laboratory-based experiments, recent developments in wireless biosensing have enabled longer, 'out-of-lab' ambulatory studies to become more common [2]. Such ambulatory methods are beneficial in that they facilitate more longitudinal and environmentally diverse EDA data collection. However, they also introduce challenges for efficiently and accurately identifying discrete skin conductance responses (SCRs) and measurement artifacts, which complicate analyses of ambulatory EDA data. Therefore, interest in developing automated systems that facilitate analysis of EDA signals has increased in recent years. Ledalab is one such system that automatically identifies SCRs and is currently considered a gold standard in the field of ambulatory EDA recording. However, Ledalab, like other current systems, cannot distinguish between SCRs and artifacts. The present manuscript describes a novel technique to accurately and efficiently identify SCRs and artifacts using curve fitting and sparse recovery methods We show that our novel approach, when applied to expertly labeled EDA data, detected 69% of the total labeled SCRs in an EDA signal compared to 45% detection ability of Ledalab. Additionally, we demonstrate that our system can distinguish between artifact and SCR shapes with an accuracy of 74%. This work, along with our previous work [3], suggests that matching pursuit is a viable methodology to quickly and accurately identify SCRs in ambulatory collected EDA data, and that artifact shapes can be separated from SCR shapes.