Abstract: Electrodermal activity (EDA) is a read-out of the body’s sympathetic nervous system measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality and emotional states. I will show that despite the complex patterns often observed in EDA measurements, the physiology of this process suggests that EDA inter-pulse intervals should be accurately described by inverse Gaussian probability models under stable background skin conditions, and by either gamma or lognormal models under fluctuating background conditions. These probability models provide accurate descriptions of EDA during quiet wakefulness and under propofol sedation and that they suggest a highly succinct point process framework for real-time tracking of sympathetically-mediated changes in physiological state.