  Heart rate variability (HRV) is a well-known phenomenon whose characteristics
are of great clinical relevance in pathophysiologic investigations. In
particular, respiration is a powerful modulator of HRV contributing to the
oscillations at highest frequency. Like almost all natural phenomena, HRV is
the result of many nonlinearly interacting processes; therefore any linear
analysis has the potential risk of underestimating, or even missing, a great
amount of information content. Recently the technique of Empirical Mode
Decomposition (EMD) has been proposed as a new tool for the analysis of
nonlinear and nonstationary data. We applied EMD analysis to decompose the
heartbeat intervals series, derived from one electrocardiographic (ECG) signal
of 13 subjects, into their components in order to identify the modes associated
with breathing. After each decomposition the mode showing the highest frequency
and the corresponding respiratory signal were Hilbert transformed and the
instantaneous phases extracted were then compared. The results obtained
indicate a synchronization of order 1:1 between the two series proving the
existence of phase and frequency coupling between the component associated with
breathing and the respiratory signal itself in all subjects.
