Toward Sensor-based Sleep Monitoring with Electrodermal Activity Measures

TL;DR

This study explores the potential of electrodermal activity (EDA) wearable sensors to monitor sleep quality and efficiency, identifying key EDA features that predict sleep metrics with promising results for wearable sleep tracking.

Contribution

It introduces a novel approach combining factor analysis and causal modeling to assess EDA data's effectiveness in sleep monitoring, highlighting two latent EDA factors as predictors.

Findings

EDA Magnitude predicts sleep efficiency effectively.

Efficacy of EDA features in classifying sleep quality shows promise.

More accurate sensors are needed for detecting smaller sleep quality changes.

Abstract

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for self-reported sleep efficiency (SE), sleep quality (SQ), and the latent EDA factors. Structural equation modeling was used to confirm fit of the extracted graph. Based on the generated graph, logistic regression and naive Bayes models were used to test the efficacy of the EDA data in predicting SE and SQ. Six EDA features extracted from the total signal over a night's sleep could be explained by two latent factors, EDA Magnitude and EDA Storms. EDA Magnitude performed as a strong predictor for SE to aid detection of substantial changes in time asleep. The performance of EDA Magnitured and SE in classifying SQ showed promise for wearable sleep monitoring applications. However, our data suggest that obtaining a more accurate sensor-based measure of SE will be necessary before smaller changes in SQ can be detected from EDA sensor data alone.