Sympathovagal balance: a critical appraisal.

Abstract

Given the importance of the autonomic nervous system to cardiovascular health, it is not surprising that there is and has been great interest in measurements of human sympathetic and vagus nerve traffic as tools that might inform physiological and pathophysiological mechanisms. Pagani and coworkers1 advanced the provocative notion that the instantaneous balance between sympathetic and vagal nerve activities can be captured by a single number, obtained by dividing RR-interval spectral power centered at ≈0.1 Hz by spectral power centered at higher, primarily respiratory frequencies. This ratio, or sympathovagal balance, has been embraced with great enthusiasm2 because it offers new possibilities for understanding dynamic, critically important autonomic interrelations in humans by the use of totally noninvasive, unobtrusive means.3 The broad bases for this mathematical treatment are as follows: (1) 0.1-Hz RR intervals are importantly mediated by fluctuations of sympathetic nerve activity; (2) higher-frequency RR-interval rhythms are mediated almost exclusively by fluctuations of vagal-cardiac nerve activity; and (3) physiological interventions tend to provoke reciprocal changes of sympathetic and vagal neural outflows. Sympathovagal balance, the ratio of these periodicities, is taken to reflect the balance between the opposing neural mechanisms. This review examines the physiological foundations of sympathovagal balance. The ECG is recorded with the subject in a steady state (when rhythms are stationary) for a period sufficiently long to define events occurring over frequencies of interest. RR-interval spectral power is calculated from this series of intervals with an autoregressive algorithm, which yields center frequencies and absolute power of component fluctuations, based on a model whose order (the number of parameters) is selected automatically to minimize Akaike’s information criterion statistic.4 (The statistical uncertainty and consequences of the automatic selection of the autoregressive model have not been defined fully; however, it is clear that the model order importantly determines both …