Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal.
Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ([Formula: see text]). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT).
Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ( SNRimp) and lower mean squared error ( MSE) when compared with that of the three previous methods (averaged [Formula: see text], and MSE = 0.028 on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68.
Conclusion: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails.
Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data.