Separation of heart rate variability components of the autonomic nervous system by utilizing principal dynamic modes

This work introduces a modified Principal Dynamic Modes (PDM) methodology using eigenvalue/eigenvector analysis to separate individual components of the sympathetic and parasympathetic nervous contributions to heart rate variability. We have modified the PDM technique to be used with even a single output signal of heart rate variability data, whereas the original PDMs required both input and output data. This method specifically accounts for the inherent nonlinear dynamics of heart rate control, which the current method of power spectrum density (PSD) is unable to do. Propranolol and atropine were administered to normal human volunteers intravenously to inhibit the sympathetic and parasympathetic activities, respectively. With separate applications of the respective drugs, we found a significant decrease in the amplitude of the waveforms that correspond to each nervous activity. Furthermore, we observed near complete elimination of these dynamics when both drugs were given to the subjects. Comparison of our method to the conventional low/high frequency ratio of PSD shows that PDM methodology provides much more accurate assessment of the autonomic nervous balance by separation of individual components of the autonomic nervous activities. The PDM methodology is expected to have an added benefit that diagnosis and prognostication of a patient's health can be determined simply via a non-invasive electrocardiogram.