A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
This addresses the difficulty in extracting physiological signals from EDA data for applications like stress analysis and seizure detection, representing an incremental improvement over prior methods.
The paper tackles the problem of analyzing Electrodermal Activity (EDA) signals, which are obscured by noise components, by proposing a compressed sensing-based decomposition method. The result is more accurate recovery of user responses compared to existing techniques, as demonstrated on synthetic and real-world data.
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.