An automated sympathetic neurogram analysis to preserve beat-by-beat signal
integrity, independent of signal quality.
Hamner, J. W., and J. Andrew Taylor.
Laboratory for Cardiovascular Research, Research and Training Institute, Hebrew
Rehabilitation Center for the Aged, Boston, 02131; and Division on Aging, Harvard
Medical School, Boston, Massachusetts 02215
APStracts 8:0252A, 2001.
Sympathetic nerve activity (SNA) can provide critical information on cardiovascular
regulation; however, in a typical laboratory setting, adequate recordings 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 evaluation, 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 during both steady-state
conditions (17 subjects) and dynamic changes with rapid vasoactive drug infusion (14
recordings from 5 subjects) on SNA signals of widely varied quality. Automated
measures of SNA were highly correlated to visual measures of steady-state activity (r =
0.903, P < 0.001), dynamic 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 analysis provides a viable alternative to tedious and subjective visual
analyses, while maximizing the usability of noisy nerve tracings.
Received 2 January 2001; accepted in final form 19 April 2001
APS Manuscript Number A0003-1.
Article publication pending J Appl Physiol
ISSN 1080-4757 Copyright 2001 The American Physiological Society.
Published in APStracts on 18 June 2001