Detecting Sympathetic Discharges: Comparison of Electrodermal Activity and Skin Sympathetic Nerve Activity in Stimulation-to-Response Time and Recovery Time to Baseline

The sympathetic nervous system (SNS) is pivotal in cardiovascular regulation. Skin sympathetic nerve activity (SKNA), extracted from electrocardiogram (ECG), is an innovative noninvasive metric for assessing SNS, traditionally evaluated via electrodermal activity (EDA). Identifying sparse drivers—markers of neural discharge onsets in EDA or SKNA signals—enhances SNS analysis. We employed the SparsEDA algorithm to identify SKNA drivers and compared them with EDA phasic drivers derived from cvxEDA and SparsEDA algorithms. Our evaluation included burst detection performance and temporal features: stimulus-to-response time and recovery time. In a thermal grill pain experiment with sixteen subjects, each undergoing six SNS stimulations, we concurrently recorded EDA and SKNA signals. SKNA demonstrated superior performance with a 97% hit rate, 100% recovery rate, and minimal false alarms during control (0.20±0.77) and interstimulus periods (1.27±1.44). In contrast, EDA drivers had hit and recovery rates below 80% and 50%, respectively, and a higher number of false alarms during interstimulus times (SparsEDA: 11.36±5.24; cvxEDA: 10.00±5.88). SKNA drivers closely matched annotated labels, achieving a root mean square error (RMSE) of 0.42 s, outperforming SparsEDA (RMSE: 5.09 s) and cvxEDA (RMSE: 3.73 s) drivers. The SKNA recovery time averaged 5.59±2.44 seconds, closely aligning with the actual pain stimulus duration (∼5 s), whereas EDA recovery times were nearly three times longer. The superior accuracy of SKNA drivers may result from directly measuring nerve activity, unlike EDA signals influenced by sweat pore hydrodynamics. Therefore, SKNA, with its precise onset response and quicker recovery time, holds promise for diverse applications in SNS assessment.