Electrodermal Activity Is Sensitive to Sleep Deprivation but Does Not Moderate the Effect of Total Sleep Deprivation on Affect

Towards real-world wearable sleepiness detection: Electrodermal activity data during speech can identify sleep deprivation

Accurate assessment of sleepiness is pivotal in managing the fatigue-associated risks stemming from sleep deprivation. Speech signals are easy to obtain, allowing detection of sleepiness anywhere. Previous machine learning (ML) studies using speech have not been successful in achieving reliable estimation of perceived sleepiness levels, which results in inaccurate sleepiness determination. In this paper, we propose that these challenges primarily stem from the inherent complexities of speech signals with inaccurate labels of sleepiness. Because the physical effects of sleepiness become pronounced after prolonged wakefulness, we conducted a 25-h sleep deprivation study. We collected electrodermal activity (EDA) and speech data from 30 subjects during speech production every 2 h over the 25-hour period, along with various sleepiness level labels-their cognitive impairment scores derived from the psychomotor vigilance test, their self-reported sleepiness scores, and the h awake scores. The data analysis compared EDA recorded during speech versus only the speech data and examined which approach provided better sleepiness level estimation and detection using ML. The ML result is that features derived from only EDA during speech production provided the most accurate sleepiness determination. Specifically, EDA ML models trained using the hours awake scores provided the best sleepiness level estimation, with 0.53 correlation, and better detection of sleepiness (which is related to cognitive performance deterioration), with 0.85 accuracy (0.80 sensitivity), when compared to ML features derived from speech, which obtained 0.40 correlation for sleepiness level estimation and 0.69 accuracy (0.59 sensitivity) for sleepiness detection. Moreover, the EDA data collected during speech production offered the best performance for sleepiness detection compared to EDA collected during other activities, such as visual vigilance (0.68 accuracy and 0.65 sensitivity). Given the potential of EDA data during speech production, this work demonstrates the promise of future wearable devices that could collect EDA data from speech activity, along with speech signals, for more advanced and accurate real-world sleepiness detection.

Adaptive thresholding increases sensitivity to detect changes in the rate of skin conductance responses to psychologically arousing stimuli in both laboratory and ambulatory settings

Psychophysiologists recording electrodermal activity (EDA) often derive measures of slow, tonic activity-skin conductance level (SCL)-and faster, more punctate changes-skin conductance responses (SCRs). A SCR is conventionally considered to have occurred when the local amplitude of the EDA signal exceeds a researcher-determined threshold (e.g., 0.05 μS), typically fixed across study participants and conditions. However, fixed SCR thresholds can preferentially exclude data from individuals with low SCL because their SCRs are smaller on average, thereby reducing statistical power for group-level analyses. Thus, we developed a fixed plus adaptive (FA) thresholding method that adjusts identification of SCRs based on an individual's SC at the onset of the SCR to increase statistical power and include data from more participants. We assess the utility of applying FA thresholding across two independent samples and explore age and race-related associations with EDA outcomes. Study 1 uses wired EDA measurements from 254 healthy adults responding to evocative images and sounds in a laboratory setting. Study 2 uses wireless EDA measurements from 20 children with autism in a clinical environment while they completed behavioral tasks. Compared to a 0.01, 0.03, and 0.05 μS fixed threshold, FA thresholding at 1.9% modestly increases statistical power to detect a difference in SCR rate between tasks with higher vs. lower subjective arousal and reduces exclusion of participants by up to 5% across both samples. This novel method expands the EDA analytical toolbox and may be useful in populations with highly variable basal SCL or when comparing groups with different basal SCL. Future research should test for reproducibility and generalizability in other tasks, samples, and contexts. IMPACT STATEMENTS: This article is important because it introduces a novel method to enhance sensitivity and statistical power in analyses of skin conductance responses from electrodermal data.