Electrodermal Activity as a Biomarker in Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Obsessive-Compulsive Disorder: A Systematic Review
TL;DR
Overall, standardized protocols and larger multimodal longitudinal studies are needed to establish EDA as a clinically useful biomarker for disorder assessment and personalized interventions.
Abstract
Electrodermal activity (EDA) has emerged as a promising physiological measure for objectively assessing neurodevelopmental disorders (NDDs) and related disorders, yet its effectiveness across conditions remains unclear. Following PRISMA guidelines, this review analyzed 23 studies (ASD: 11 studies, 410 participants; ADHD: 7 studies, 900 participants; OCD: 5 studies, 154 participants) published between 2010 and 2024 with a focus on methodology, including measurement protocols, signal processing, and analytical approaches. EDA demonstrated utility for diagnostic differentiation, predictive modeling, and physiological evaluation across conditions, with consistent patterns of heightened social arousal in ASD, medication-responsive sympathetic differences in ADHD, and impaired fear extinction in OCD. Machine learning approaches improved performance when combining EDA with multimodal physiological measures. However, substantial methodological heterogeneity existed across devices, recording sites, sampling frequencies, and signal processing, with 11 studies lacking signal processing details and 10 omitting sampling frequencies. The absence of standardized cross-disorder comparisons limited identification of disorder-specific versus shared autonomic signatures. While wearable technologies can enable continuous real-world monitoring, motion artifacts and signal quality remain challenges. Overall, standardized protocols and larger multimodal longitudinal studies are needed to establish EDA as a clinically useful biomarker for disorder assessment and personalized interventions.