Key result
A novel compressed sensing-based decomposition approach achieved an AUC of 0.848 for detecting skin conductance response events at 4 Hz, outperforming existing methods like cvxEDA (AUC 0.622) and Ledalab (AUC 0.817).
Absolute Event Rate: 0.848% vs 0.622%
A novel compressed sensing-based decomposition framework accurately recovers skin conductance response events from electrodermal activity signals by mitigating baseline shifts and noise.
May improve efficiency of autonomic monitoring; leaves open clinical adoption pending prospective validation.
The measurement and analysis of electrodermal activity (EDA) offers applications in diverse areas ranging from market research to seizure detection and to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components that can obscure the signal information related to a user's response to a stimulus. We show how simple preprocessing 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 with the existing techniques.
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Jain et al. (2016) studied Healthy subjects (video stimulus experiment) (n=9). Compressed sensing based decomposition vs. cvxEDA, Ledalab, and raw signal was evaluated on Area Under the Curve (AUC) for SCR event detection at 4 Hz. A novel compressed sensing-based decomposition approach achieved an AUC of 0.848 for detecting skin conductance response events at 4 Hz, outperforming existing methods like cvxEDA (AUC 0.622) and Ledalab (AUC 0.817).
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