Sano, A., Picard, R.W., Kaliouby R., Malow, B., Goldman, S. "Autonomic Sleep Patterns in Children with Autism Spectrum Disorders," in the Extended Abstract of IMFAR 2011, San Diego, CA, USA, May 12-14. 2011.

Abstract

Background
Children diagnosed with autism spectrum disorders (ASD) often suffer from sleep
disorders such as insomnia, which generates long sleep latency and fragmented sleep. Sleep
disorders reduce children’s concentration for learning and contribute to increased stress for them
and their families. Polysomnography (PSG) is a gold standard to evaluate and diagnose sleep
patterns, but the sensors tend to be uncomfortable and expensive, and may interfere with sleep.
Actigraphy is a non-invasive method to evaluate daytime and sleep activity with a
wrist device. In addition, we have developed a wireless non-invasive sensor to measure
electrodermal activity (EDA) to observe sympathetic nervous activity. Combining actigraphy
and EDA can provide details of children’s sleep and can be comfortably used for low-cost sleep
monitoring at home.

Objectives
We aimed to evaluate sleep patterns in children with ASD using both PSG and a
wearable sensor that enables comfortable measurement of EDA through skin conductance, skin
temperature, and actigraphy on the wrist.

Methods
Six children diagnosed with ASD (ages 3-8) participated in overnight measurement in
a sleep lab. One group (N=3) were good sleepers, who took melatonin before sleep and the
other group (N=3) were poor sleepers. We examined skin conductance, actigraphy, and skin
temperature during sleep from the inside left and right wrists (N=5, only right wrist for N=1)
and compared the behavior of these signals to PSG. We obtained thirty-second epochs of
labeled sleep stages (Wake, REM, Stage1, 2 and 3).
The data was analyzed as follows:
1. Pre-processing: zero-crossing and Cole's function were applied to the accelerometer
data to discriminate between sleep and wake. EDA data was low-pass filtered (0.4 Hz).
2. We compared the amplitude of left and right EDA in sleep stages.
3. We analyzed “Storm” regions with high-frequency EDA, more than 6 peaks/min. We
counted the number of storms per night as well as the number of peaks per storm. We also
calculated areas, durations of storms and onset intervals between storms. We compared these
storm characteristics to sleep stages.

Results
Four out of five subjects showed that EDA on the left wrist was higher than that on the
right wrist. Most EDA storm patterns occurred during stage 2 and stage 3 (slow-wave) sleep.
Larger amplitude “storms” occurred earlier in the evening. The poor sleepers had shortened
latency of the first storm (it came earlier for poor sleepers).

Conclusions
We measured continuous EDA, actigraphy and skin temperature on children diagnosed
with ASD with a comfortable wearable sensor and evaluated the relationship between EDA characteristics, laterality, and sleep stages from simultaneously recorded PSG. 

On most children, EDA on the left wrist was higher than EDA on the right wrist. Moreover, EDA showed characteristic high-frequency storms that occurred during stage 2 and stage 3 (slow-wave sleep) with larger areas under the curve earlier in the evening. The group of poor sleepers showed shorter latency of the first storms than the group of good sleepers. The comfortable wearable sensor showed new sleep characteristics on children diagnosed with ASD that could be measured easily at home.