Skin Conductance Response Artifact Reduction: Leveraging Accelerometer Noise Reference and Deep Breath Detection

Electrodermal activity (EDA) shows a significant correlation with activation of the autonomic nervous system (ANS) activation. Regular ambulatory monitoring via wearables and consequent inference of ANS activation has a wide range of applications tracking mental health. The real-world implementation of a closed-loop system to regulate one’s emotional state to improve their mental well-being requires an accurate and reliable estimation of ANS activation in ambulatory settings. However, the presence of motion artifacts in skin conductance (SC) data collected in ambulatory settings makes the analysis for such estimation unreliable. We propose a multi-rate adaptive filtering scheme to reduce motion artifacts in SC data that utilizes three-axis accelerometer data. We investigate four types of linear and nonlinear adaptive filters. We use both simulated and experimental data to investigate the performance of adaptive filters. Furthermore, we utilize the respiration signal to identify the probability of respiration-induced SC artifacts. Next, we use a Bayesian filter-based deconvolution approach to identify SC responses (SCRs) induced by underlying arousal events and deep breaths. Finally, we propose to use the respiration signal to separate the artifacts in SC due to deep breaths. Our results show that linear finite impulse response least squares recursive filters perform best among the four types of adaptive filters studied. We draw this conclusion by obtaining receiver operating characteristics of event-related SCRs detection with deconvolution after artifact reduction with different adaptive filters. Moreover, for all of our simulated and experimental datasets investigated in this study, we observe that the recursive least-squares filter always provides stable results. Additionally, our results show our ability to detect respiration-induced SCRs and the corresponding activation of ANS. The evaluation of adaptive filters shows the potential to utilize reference signals for successful artifact modeling and reduction. Effective artifact reduction will lead to reliable ANS activation monitoring and consequent robust implementation of a closed-loop wearable machine interface architecture to eventually improve one’s mental health.