An open-source application for the standardized burst identification from the integrated muscle sympathetic neurogram

Muscle sympathetic nerve activity (MSNA) can be acquired from humans using the technique of microneurography. The resulting integrated neurogram displays pulse-synchronous bursts of sympathetic activity, which undergoes processing for standard MSNA metrics including burst frequency, height, area, incidence, total activity, and latency. The procedure for detecting bursts of MSNA and calculating burst metrics is tedious and differs widely among laboratories worldwide. We sought to develop an open-source, cross-platform web application that provides a standardized approach for burst identification and a tool to increase research reproducibility for those measuring MSNA. We compared the performance of this web application against a manual scoring approach under conditions of rest, chemoreflex activation (n = 9, 20-min isocapnic hypoxia), and metaboreflex activation (n = 13, 2-min isometric handgrip exercise and 4-min postexercise circulatory occlusion). The intraclass correlation coefficient (ICC) indicated good to strong agreement between scoring approaches for burst frequency (ICC = 0.92-0.99), incidence (ICC = 0.94-0.99), height (ICC = 0.76-0.88), total activity (ICC = 0.85-0.99), and latency (ICC = 0.97-0.99). Agreement with burst area was poor to moderate (ICC = 0.04-0.67) but changes in burst area were similar with chemoreflex and metaboreflex activation. Scoring using the web application was highly efficient and provided data visualization tools that expedited data processing and the analysis of MSNA. We recommend the open-source web application be adopted by the community for the analysis of MSNA.NEW & NOTEWORTHY The basic analysis of muscle sympathetic nerve activity (MSNA) requires the identification of pulse-synchronous bursts from the integrated neurogram before standard MSNA metrics can be quantified. This process is a time-consuming task requiring an experienced microneurographer to visually identify and manually label bursts. We developed an open-source, cross-platform application permitting a standardized approach for sympathetic burst identification and present the performance of this application against a manual scorer under basal conditions and during sympathoexcitatory stresses.