Model based concept to extract heart beat-to-beat variations beyond respiratory arrhythmia and baroreflex

The heart rate (HR) and its variability (HRV) reflect modulation of the autonomous nervous system, especially sympathovagal balance. The aim of this research was to develop a personalizable HR model and an in-silico system that could identify HR regulation parameters associated with respiratory arrhythmia (RSA) and baroreflex, and subsequently capture the residual heart beat-to-beat variations from individual psychophysiological recordings in humans. Here respiration signal, blood pressure signal and time instances of R peaks of EKG are used as input for the model. The model considers traditional lower-order mechanisms of HR dynamic, extracting residual displacements of the modeled R peaks relative to real R peaks. Three components - tonic, spontaneous and 0.1 Hz changes - can be seen in these R peak displacements. These dynamic residuals can help to analyze HRV beyond RSA and baroreflex, whereas our model-based concept suggests that the residuals are not merely modeling errors. The proposed method could help to investigate the presumably additional neural regulation impulses from higher-order brain and other influences. ### Competing Interest Statement The authors have declared no competing interest.