A Compressed Sensing Based Decomposition of Electrodermal Activity Signals

TL;DR

This paper introduces a novel compressed sensing approach to decompose Electrodermal Activity signals, effectively separating physiological responses from noise, with proven bounds and improved accuracy demonstrated on synthetic and real data.

Contribution

The paper presents a new compressed sensing based framework for EDA signal decomposition with theoretical recovery guarantees and superior performance over existing methods.

Findings

Enhanced recovery of user responses in EDA signals

Proven bounds on signal decomposition accuracy

Improved results on both synthetic and real-world data

Abstract

The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components which can obscure the signal information related to a user's response to a stimulus. We show how simple pre-processing followed by a novel compressed sensing based decomposition can mitigate the effects of the undesired noise components and help reveal the underlying physiological signal. The proposed framework allows for decomposition of EDA signals with provable bounds on the recovery of user responses. We test our procedure on both synthetic and real-world EDA signals from wearable sensors and demonstrate that our approach allows for more accurate recovery of user responses as compared to the existing techniques.