Comparative Analysis of ECG-derived Skin Nerve Activity and Electrodermal Activity for Assessing Sympathetic Activity

Recently, it has been shown that skin sympathetic nerve activity (SKNA) can be obtained using electrocardiogram (ECG) signals at a high sampling rate. More importantly, recent studies have demonstrated that SKNA can be used as a surrogate noninvasive measure for assessing the sympathetic nervous system (SNS) activity. The electrodermal activity (EDA), which is a measure of changes in skin conductance due to sympathetic innervation, has also been shown to be a noninvasive physiomarker of SNS. For example, EDA has successfully shown its sensitivity and classification accuracy in detecting sympathetic elevation due to pain and emotion arousals. Given these two distinct approaches to noninvasive assessment of SNS, we directly compared and evaluated the effectiveness of ECG-derived SKNA and EDA in assessing SNS activity. Moreover, we examined if there is a direct correlation between SKNA and EDA given that both measurements provide SNS information. To this end, nine participants were recruited for Valsalva maneuver (VM) and thermal grill pain tests, while simultaneously recording ECG and EDA. Four features were derived from each of the integral area of rectified SKNA (iSKNA) and the phasic component of EDA signals (EDA(phasic)): peak amplitude, average amplitude, energy, and standard deviation. We then calculated Fisher's ratio and area under the receiver operating characteristic curve (AUROC) for each feature. Peak amplitude and standard deviation of iSKNA showed significant difference between baseline and post-stimulation. Standard deviation of iSKNA showed a higher Fisher's ratio and AUROC than any other EDA and SKNA features. EDA(phasic) showed a delay of 4+ seconds from the onset of the stimulation, compared to SKNA. Moreover, by applying a larger window to extract iSKNA, we obtained Pearson correlation coefficient value of 0.78 +/- 0.22 between iSKNA and EDA(phasic) in VM test. In conclusion, SKNA was a better discriminative measure than EDA in obtaining SNS information. In addition, EDA dynamics can be derived from SKNA with the latter providing better onset and endpoint of SNS dynamics than the former.