Empirical mode decomposition to assess cardiovascular autonomic control in rats

Abstract

Heart beat rate and blood pressure, together with baroreflex sensitivity, have become important tools in assessing cardiac autonomic system control and in studying sympathovagal balance. These analyses are usually performed thanks to spectral indices computed from standard spectral analysis techniques. However, standard spectral analysis and its corresponding rigid band-pass filter formulation suffer from two major drawbacks. It can be significantly distorted by non-stationarity issues and it proves unable to adjust to natural intra- and inter-individual variability. Empirical mode decomposition (EMD), a tool recently introduced in the literature, provides us with a signal-adaptive decomposition that proves useful for the analysis of non-stationary data and shows a strong capability to precisely adjust to the spectral content of the analyzed data. It is based on the concept that any complicated set of data can be decomposed into a finite number of components, called intrinsic mode functions, associated with different spectral contributions. The aims of this study were twofold. First, we studied the changes in the sympathovagal balance induced by various pharmacological blockades (phentolamine, atropine and atenolol) of the autonomic nervous system in normotensive rats. Secondly, we assessed the use of EMD for the analysis of the cardiac sympathovagal balance after pharmacological injections. For this, we developed a new (EMD-based) low frequency vs. high frequency spectral decomposition of heart beat variability and systolic blood pressure, we define the corresponding EMD spectral indices and study their relevance to detect and analyze changes accurately in the sympathovagal balance without having recourse to any a priori fixed high-pass/low-pass filters.