Objective Assessment of Acute Stress Disorder in Female Subjects Using Wearable Measures of Electrodermal Activity

• Published in: 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2024; pp. 1 - 4
• Main Authors: Peitzsch, Andrew G., Kong, Youngsun, Posada-Quintero, Hugo F., Chon, Ki H.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2024
• Subjects: Accuracy; Acute Stress Disorder; Adult; Current measurement; electrodermal activity; Female; Galvanic Skin Response; Humans; Machine Learning; Physiology; Predictive models; Reliability; Skin; Stress; Stress Disorders, Post-Traumatic - diagnosis; Stress Disorders, Post-Traumatic - physiopathology; Stress Disorders, Traumatic, Acute - diagnosis; Stress Disorders, Traumatic, Acute - physiopathology; Stress measurement; Sympathetic nervous system; Time-frequency analysis; wearable devices; Wearable Electronic Devices
• ISSN: 2694-0604
• DOI: 10.1109/EMBC53108.2024.10782849

Acute Stress Disorder (ASD) is a precursor disease to Post Traumatic Stress Disorder (PTSD), occurring within the first month after trauma and may serve as a vital timepoint for treating chronic PTSD. Current ASD diagnosis is resource intensive and may miss vulnerable patients in early prescreening steps, particularly female patients who are known to have higher rates of PTSD and a higher conversion rate from ASD to PTSD. We propose a new ASD diagnostic tool based on electrodermal activity (EDA) to measure changes in sympathetic nervous system (SNS) activity post-trauma. Using both traditional phasic decomposition and time-frequency decomposition methods, we are able to detect key differences in ASD and non-ASD groups within 72 hours of trauma. We compared several machine learning models to determine optimal diagnostic capability. Our logistic regression model was able detect ASD in female subjects with a balanced accuracy of 0.72 and an F1 score of 0.78. Our time-frequency decomposition techniques are highly sensitive to SNS changes post trauma. Future study on EDA dynamics in the early stages of trauma may help improve detection of ASD and increase PTSD treatment outcomes.