The Valsalva maneuver revisited by wavelets
Xavier, R.; Laranjo, S.ér.; Ducla-Soares, E.; Andrade, A.; Boto, J.P.; Santos-Bento, M.; Ducla-Soares, J.é L.; Carvalho, L.ís.S.; Rocha, I.
Revista Portuguesa de Cardiologia Orgao Oficial da Sociedade Portuguesa de Cardiologia 27(4): 435-441
2008
ISSN/ISBN: 0870-2551 PMID: 18605062 Document Number: 615658
The Valsalva maneuver is an autonomic test that evokes short sharp cardiovascular fluctuations mediated by the autonomic nervous system. Numerous spectral analysis methods have been proposed to analyze biological signals. When applied to heart rate (HR) variability, two major bands related to autonomic influence have been defined: LF (mainly sympathetic) and HF (parasympathetic). However, conventional spectral approaches are based on the assumption of stationarity, and most require at least five minutes of recording. These two requirements cannot be fulfilled when analysis of dynamic processes such as the regulatory action of the autonomic nervous system is required. Wavelet transform is a mathematical tool that, by determining the temporal localization of the changes, the frequencies involved and their contribution to the entire signal, overcomes the limitations imposed by conventional methods. In the present work, we use wavelets to evaluate autonomic influence through the LF and HF band powers on acute changes in systolic blood pressure (sBP) and RR intervals (RRI) during the Valsalva maneuver. Eighteen healthy volunteers performed the maneuver by blowing, after a deep inspiration and with a closed glottis, against a pressure of 40 mmHg for 15 seconds. Data were analyzed in three different periods: 1) the last minute just prior to the test (CTR); 2) the 15 seconds of the Valsalva maneuver (VM); 3) during the next 35 seconds after the maneuver (aVM). We observed that LF power increased in sBP and RRI in both VM and ower only increased after Valsalva. The data showed a marked increase in sympathetic activity during and after the maneuver and an increase in parasympathetic outflow after aVM. In conclusion, the ability of wavelets to analyze short non-stationary signals makes wavelet transform a promising tool to evaluate physiological and pathological autonomic conditions.