Wearable Electrodermal Activity Reveals Sustained Sympathetic Responses to Sleep Arousals

Introduction: Sympathetic responses to sleep arousals are well documented through cardiac measures, yet capturing autonomic physiology across additional channels offers a more complete picture of the body's reaction to these brief events. Electrodermal activity (EDA), driven by sympathetic sudomotor pathways, offers a distinct and largely unexplored channel to characterize autonomic responses during sleep arousals. Methods: To characterize autonomic reactivity during sleep arousals, we analyzed synchronous polysomnography and wrist-worn EDA recordings from 100 adults of the DREAMT dataset. We applied a time-varying frequency decomposition framework to isolate sleep-specific sympathetic components, extracting statistical and peak-based features from arousal events and matched stable-sleep controls. By concurrently assessing heart rate, we captured a multimodal profile of the autonomic arousal response. Finally, we evaluated the modulatory effects of arousal duration and sleep stage on EDA, alongside the confounding influence of wrist movement. Results: Relative to stable-sleep controls, arousals elicited a robust, sustained sympathetic EDA modulation that persisted up to 40 s post-arousal. In contrast, heart rate exhibited a sharp, transient increase that rapidly returned to baseline following the event. We observed that arousal duration significantly modulated sudomotor reactivity, with long arousals generating prominent responses compared to the weaker activity elicited by short arousals. Furthermore, while EDA feature trajectories remained consistent across REM and NREM sleep, REM periods introduced greater signal variability. Crucially, wrist movement did not fundamentally drive the EDA response as sympathetic activation persisted during minimal-movement arousals, though concurrent movement amplified the signal. Ultimately, our analysis revealed that this prolonged EDA activation is primarily driven by clustered sympathetic bursts and amplitude enhancement, rather than shifts in peak frequency. Conclusion: Our findings underscore the value of multimodal perspective, demonstrating that wearable EDA captures autonomic information distinct from conventional cardiac measures. Together, these signals establish a comprehensive, quantitative baseline for tracking sympathetic dynamics during sleep arousals.