Quantitative analysis of wrist electrodermal activity during sleep

Highlights

• We present the first characterization of sleep electrodermal activity with dry electrodes.

• We compare thresholds for detecting EDA peaks and establish criteria for EDA storms.

• More than 80% of the EDA peaks occurred during slow-wave and non-REM stage 2 sleep.

• Amplitude is higher in SWS than in other sleep stages.

• EDA levels and peaks were more pronounced on the wrist than on the palm.

Abstract

We present the first quantitative characterization of electrodermal activity (EDA) patterns on the wrists of healthy adults during sleep using dry electrodes. We compare the new results on the wrist to the prior findings on palmar or finger EDA by characterizing data measured from 80 nights of sleep consisting of 9 nights of wrist and palm EDA from 9 healthy adults sleeping at home, 56 nights of wrist and palm EDA from one healthy adult sleeping at home, and 15 nights of wrist EDA from 15 healthy adults in a sleep laboratory, with the latter compared to concurrent polysomnography. While high frequency patterns of EDA called “storms” were identified by eye in the 1960s, we systematically compare thresholds for automatically detecting EDA peaks and establish criteria for EDA storms. We found that more than 80% of the EDA peaks occurred in non-REM sleep, specifically during slow-wave sleep (SWS) and non-REM stage 2 sleep (NREM2). Also, EDA amplitude is higher in SWS than in other sleep stages. Longer EDA storms were more likely to occur in the first two quarters of sleep and during SWS and NREM2. We also found from the home studies (65 nights) that EDA levels were higher and the skin conductance peaks were larger and more frequent when measured on the wrist than when measured on the palm. These EDA high frequency peaks and high amplitude were sometimes associated with higher skin temperature, but more work is needed looking at neurological and other EDA elicitors in order to elucidate their complete behavior.

Introduction

Electrodermal activity (EDA) is widely used in psychophysiology and provides a measure of activity in the sympathetic nervous system, one of the main branches of the autonomic nervous system. Studies of EDA during sleep have shown that elevated levels of EDA, with high frequency “storm” patterns, are more common during deep, slow wave sleep (SWS) (Koumans et al., 1968), while the frequency of EDA peaks is lower in the first cycle of the night (Freixa i Baqué et al., 1983) (Table 1). Classically, EDA has been measured as skin conductance levels or skin conductance responses and involves attaching wired and gelled electrodes to the skin, usually on the fingers or palms (Boucsein, 1992, Fowles et al., 1981). However, several studies have shown a valid measurement of EDA on other locations including the forearm (Table 2). Studies using dry electrodes on the forearm have demonstrated reliable long-term measures of EDA (Poh et al., 2010) and have also led to the discovery of correlations between EDA and significant neurological events measured from EEG (Poh et al., 2012).

In this study, we used a wireless non-invasive EDA sensor worn as a wristband on the distal forearm, which made it easy for subjects to be monitored in the same manner in the sleep lab and at home. We collected and analyzed 80 nights of EDA data more than ever previously reported in a single study.

Our paper makes three main contributions. First, we compare wrist EDA (convenient for continuous long-term measurement) to palmar EDA (inconvenient). When we began this work, there was concern that the wrist measures would primarily reflect thermal sweating. Our work is the first to find significant EDA patterns in sleep from the forearm while simultaneously measuring skin temperature at the same position.

Second, we characterize EDA in natural sleep, proposing an automated method to extract features from the EDA and using these features to create a taxonomy of EDA patterns during sleep. For 15 nights where we have concurrent synchronized polysomnography (PSG), we also characterize the EDA–PSG relationships and compare the new measures with results published in the 1960–1970s. PSG is currently the gold standard to evaluate and diagnose sleep patterns; however, the use of PSG requires scalp EEG electrodes and other sensors that tend to be uncomfortable and expensive, time-consuming to apply, and arguably interfere with the sleep they are measuring. Actigraphy is a much less invasive method often used to estimate daytime and sleep activity with a wrist-worn device; however, it does not measure neural activity such as stages of sleep. In this study, we measure both EDA and actigraphy to develop a quantitative characterization of EDA in natural sleep.

Lastly, we also compare EDA responses with skin temperature. It has long been recognized that thermoregulatory processes are suppressed during REM, while they persist during NREM (Adam et al., 1986). In a study of five healthy men, the largest sweating, averaged across multiple sites on the body, was recorded during SWS while the lowest was recorded during REM, although sweating was not completely blocked during REM (Sagot et al., 1987). But this occurred in the absence of significant changes in skin temperature across sleep stages. We provide the first characterization of the interaction between wrist/palm EDA, skin temperature, and sleep stages.