Sano, A., Picard, R.W., Wang H.H., Stickgold, B. "Autonomic Sleep Patterns in Visual Discrimination Task Improvement," SLEEP 2011 25th Annual Meeting of the Associated Professional Sleep Societies (APSS)

Abstract

Introduction
We evaluated sleep patterns following training on a visual discrimination task
(VDT) using both polysomnography (PSG) and a wrist-worn Affectiva Q™ sensor that
enables long-term monitoring of electrodermal activity (EDA) through skin
conductance, skin temperature, and actigraphy.

Methods
Twenty-four healthy university students (ages 18-22, 16 males) participated in
three nights of measurements: in a “homey” sleep laboratory, a hospital GCRC, and at
home. Each night (PM), they trained on a different version of the VDT, slept, and were
tested the next morning (AM). Sleep in the sleep lab and GCRC were monitored with
standard PSG. We evaluated task improvement by overnight change in threshold
performance. EDA “storms” were identified when EDA exhibited >6 peaks per minute.
We obtained standard PSG sleep staging as well as both subjective and objective sleep
quality evaluations. We analyzed the correlation between EDA storms and task
improvement, sleep stage times and sleep quality.

Results

For hospital and lab nights, the time to the first EDA storm onset was
correlated with the time to SWS (p<.05) as well as with overnight task improvement
(p<.05). In seven subjects for whom precisely synchronized PSG and EDA data were
available, objective sleep quality (sleep efficiency) was correlated with the number of
EDA peaks in the first quartile of the night, while subjective sleep quality was
correlated with the proportion (inversely, p<.05) and frequency (peaks/min; p<.01) of
EDA peaks in SWS over night.

Conclusions
We measured continuous EDA and actigraphy, sleep and overnight
improvement on a visual discrimination task (VDT) in healthy college students, and
found correlations between EDA storming, task improvement, sleep stages and sleep
quality (from overnight PSG). The comfortable, wearable EDA sensor predicted
overnight VDT improvement while used in the laboratory and the hospital. More
synchronized subject data is needed to be confident of the findings.