Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification

Abstract

As the prevalence of sleep disorders is increasing, new methods for ambulatory sleep measurement are required. This paper presents electrodermal activity in different sleep stages and a sleep detection algorithm based on electrodermal activity. We analysed electrodermal activity and polysomnographic data of 43 healthy subjects and 48 patients with sleep disorders. Electrodermal activity was measured using an ambulatory device worn at the wrist. Two parameters to describe electrodermal activity were defined based on previous literature: EDASEF (electrodermal activity-smoothed feature) as parameter for skin conductance level; and EDAcounts (number of electrodermal activity-peaks) as skin conductance responses. Analysis of variance indicated significant EDASEF differences between the sleep stages wake versus N1, wake versus N2, wake versus slow-wave sleep, and wake versus rapid eye movement. The analysis of EDAcounts also showed significant differences, especially in the stages slow-wave sleep versus rapid eye movement. Between healthy subjects and patients, a significant disparity of EDAcounts was revealed in stage N1. Furthermore, the variances of EDASEF and EDAcounts in N1, N2 slow-wave sleep and rapid eye movement were higher in the patient group (p [F test] < .05). Next, an electrodermal activity-based sleep/wake discriminating algorithm was constructed. The optimized algorithm achieved an average sensitivity and specificity for sleep detection of 97% and 75%. The epoch agreement rate (average accuracy) was 86%. These outcomes are comparative to sleep detection algorithms based on actigraphy or heart rate variability. The results of this study indicate that electrodermal activity is not only a robust parameter for describing sleep, but also a potential suitable method for ambulatory sleep monitoring.

Sleep stage detection using only heart rate

“…Therefore, it is confirmed that separating N1 and N2 is necessary and effective for detecting symptoms of specific diseases. [35][36][37] Based on these results, it is important to perform five-stage sleep recognition by separating N1 and N2.…”

Dream engineering: Simulating worlds through sensory stimulation

“…This is a passive behavioral measure of sleep onset ( Kelly, Strecker, & Bianchi, 2012 ; Prerau et al, 2014 ), as loss of muscle tone is temporally tied to onset of hypnagogic imagery. Recent papers have also demonstrated that drops in heartrate and shifts in electrodermal activity (EDA) coincide with loss of muscle tone to confirm descent into hypnagogia ( Herlan, Ottenbacher, Schneider, Riemann, & Feige, 2019 ; Ogilvie, 2001 ). The user’s heart rate is monitored on the middle finger, muscle tone is tracked using a sensor wrapped around the index finger, and EDA is measured between two electrodes placed on the bottom of the wrist.…”

Neuroscience in service research: an overview and discussion of its possibilities

“…Neuro-tools can be used to monitor sleep (disturbance) and sleep quality itself (e.g. EEG and GSR; Herlan et al , 2019; Krystal and Edinger, 2008). Yet, physical (e.g.…”

“…Neuro-tools can be used to monitor sleep (disturbance) and sleep quality itself (e.g., EEG; galvanic skin response; Herlan et al, 2019;Krystal and Edinger, 2008). Yet, physical (e.g., Yu et al, 2019) and/or mental fatigue (e.g., Hopstaken et al, 2016) may be more relevant and can be captured as well using eye tracking (e.g., blinking, visual attention) and/or EEG.…”