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We use self-report and electrodermal activity (EDA) wearable sensor data from77 nights of sleep on six participants to test the efficacy of EDA data forsleep monitoring. We used factor analysis to find latent factors in the EDAdata, and causal model search to find the most probable graphical modelaccounting for self-reported sleep efficiency (SE), sleep quality (SQ), and thelatent EDA factors. Structural equation modeling was used to confirm fit of theextracted graph. Based on the generated graph, logistic regression and naiveBayes models were used to test the efficacy of the EDA data in predicting SEand SQ. Six EDA features extracted from the total signal over a night's sleepcould be explained by two latent factors, EDA Magnitude and EDA Storms. EDAMagnitude performed as a strong predictor for SE to aid detection ofsubstantial changes in time asleep. The performance of EDA Magnitured and SE inclassifying SQ showed promise for wearable sleep monitoring applications.However, our data suggest that obtaining a more accurate sensor-based measureof SE will be necessary before smaller changes in SQ can be detected from EDAsensor data alone.

Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures