Sleep Period Time Estimation Based on Electrodermal Activity

Hwang, Sang Hyun; Seo, Sangwon; Yoon, Hye-Kyung; Jung, Da Woon; Baek, Hyun Jae; Cho, Jaegeol; Choi, Jae Won; Lee, Yu Jin; Jeong, Do Un; Park, Kwang Suk

doi:10.1109/jbhi.2015.2490480

Cited by 17 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Skin temperature does rise with sleep as seen in our study group. However, sleep onset has been associated with a gradual decrease in EDA amplitude [18], while high-frequency EDA activity is noted in multiple other sleep stages including REM sleep [19]. These characteristic EDA changes were not seen in our participants.…”

Section: Discussionmentioning

confidence: 53%

Wearable Biosensors to Detect Physiologic Change During Opioid Use

et al. 2016

View full text Add to dashboard Cite

Introduction Opioid analgesic use is a major cause of morbidity and mortality in the US, yet effective treatment programs have a limited ability to detect relapse. The utility of current drug detection methods is often restricted due to their retrospective and subjective nature. Wearable biosensors have the potential to improve detection of relapse by providing objective, real time physiologic data on opioid use that can be used by treating clinicians to augment behavioral interventions. Methods Thirty emergency department (ED) patients who were prescribed intravenous opioid medication for acute pain were recruited to wear a wristband biosensor. The biosensor measured electrodermal activity, skin temperature and locomotion data, which was recorded before and after intravenous opioid administration. Hilbert transform analyses combined with paired t-tests were used to compare the biosensor data A) within subjects, before and after administration of opioids; B) between subjects, based on hand dominance, gender, and opioid use history. Results Within subjects, a significant decrease in locomotion and increase in skin temperature were consistently detected by the biosensors after opioid administration. A significant change in electrodermal activity was not consistently detected. Between subjects, biometric changes varied with level of opioid use history (heavy vs. nonheavy users), but did not vary with gender or type of opioid. Specifically, heavy users demonstrated a greater decrease in short amplitude movements (i.e. fidgeting movements) compared to non-heavy users. Conclusion A wearable biosensor showed a consistent physiologic pattern after ED opioid administration and differences between patterns of heavy and non-heavy opioid users were noted. Potential applications of biosensors to drug addiction treatment and pain management should be studied further.

show abstract

Section: Discussionmentioning

confidence: 53%

Wearable Biosensors to Detect Physiologic Change During Opioid Use

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The parameter EDASEF was defined after the specifications of Hwang et al. () who proposed using the average of the minimum and maximum value of each epoch and smoothed it over 5 epochs to obtain the “EDA‐smoothed‐EDA‐feature” (EDASEF).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, EDA differences could also be shown in a two‐channel in‐home EEG sleep classification (Onton, Kang, & Coleman, ). Sleep period time, sleep onset and offset, and long awakening periods could also be estimated satisfactorily (Hwang et al., ).…”

Section: Introductionmentioning

confidence: 99%

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

Herlan

Ottenbacher²,

Schneider

et al. 2018

Journal of Sleep Research

View full text Add to dashboard Cite

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.

show abstract

“…A total of 20 papers have only used EDA signals for stress detection [17][18][19][20]24,[27][28][29][30][31][32][33]36,42,43,[45][46][47]49,53,73,103]. The authors have focused on developing methods or evaluating ML models based in EDA and its features.…”

Section: Bio-markers Used In the Papersmentioning

confidence: 99%

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Sánchez-Reolid¹,

Mt²,

Fernández-Caballero³

2020

Preprint

View full text Add to dashboard Cite

Early detection of stress can prevent us from suffering from a long-term illness such as depression and anxiety. This article presents a scoping review of stress detection based on electrodermal activity (EDA) and machine learning (ML). From an initial set of 395 articles searched in six scientific databases, 58 were finally selected according to various criteria established. The scoping review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, preprocessing, processing and feature extraction. Finally, all the ML techniques applied to the features of this signal have been studied for stress detection. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high performance values. On the contrary, it has been evidenced that unsupervised learning is not very common in the detection of stress through EDA. This scoping review concludes that the use of EDA for the detection of arousal variation (and stress detection) is widely spread, with very good results in its prediction with the ML methods found during this review.

show abstract

Sleep Period Time Estimation Based on Electrodermal Activity

Cited by 17 publications

References 41 publications

Wearable Biosensors to Detect Physiologic Change During Opioid Use

Wearable Biosensors to Detect Physiologic Change During Opioid Use

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

Machine Learning for Stress Detection from Electrodermal Activity: A Scoping Review

Contact Info

Product

Resources

About