Automated quantification of sympathetic beat-by-beat activity, independentof signal quality

Sympathetic nerve activity (SNA) can provide critical information on cardio vascular regulation; however, in a typical laboratory setting, adequate rec ordings require assiduous effort, and otherwise high-quality recordings may be clouded by frequent baseline shifts, noise spikes, and muscle twitches. Visually analyzing this type of signal can be a tedious and subjective eva luation, whereas objective analysis through signal averaging is impossible. We propose a new automated technique to identify bursts through objective detection criteria, eliminating artifacts and preserving a beat-by-beat SNA signal for a variety of subsequent analyses. The technique was evaluated d uring both steady-state conditions (17 subjects) and dynamic changes with r apid vasoactive drug infusion (14 recordings from 5 subjects) on SNA signal s of widely varied quality. Automated measures of SNA were highly correlate d to visual measures of steady-state activity (r = 0.903, P < 0.001), dynam ic relation measures (r = 0.987, P < 0.001), and measures of burst-by-burst variability (r = 0.929, P < 0.001). This automated sympathetic neurogram a nalysis provides a viable alternative to tedious and subjective visual anal yses while maximizing the usability of noisy nerve tracings.