Abstract
We present a novel approach to quantify heart rate variability (HRV) and the results of applying this approach to synthetic and original data sets. Our approach evaluates the periodicity of heart rate by calculating the transform of Relative Shannon Entropy, the maximum value of the RR interval periodogram, and the maximum, mean values, and sample entropy of the autocorrelation function. Synthetic data were generated using a Van der Pol oscillator; and the original data were electrocardiogram (ECG) recordings from anesthetized rats after acute lung injury while on biologically variable (BVV) or continuous mechanical ventilation (CMV). Analysis of the synthetic data revealed that our measures were correlated highly to the bandwidth of the oscillator and assessed periodicity. Then, applying these analytical tools to the ECGs determined that the heart rate (HR) of BVV group had less periodicity and higher variability than the HR of the CMV group. Quantifying periodicity effectively identified a readily apparent difference in HRV during BVV and CMV that was not identified by power spectral density measures during BVV and CMV. Cardiorespiratory coupling is the probable mechanism for HRV increasing during BVV and becoming periodic during CMV. Thus, the absence or presence of periodicity in ventilation determined HRV, and this mechanism is distinctly different from the cardiorespiratory uncoupling that accounts for the loss of HRV during sepsis.