Model based concept to extract heart beat-to-beat variations beyond respiratory arrhythmia and baroreflex
Heart rate (HR) and its variability (HRV) reflect the autonomous nervous system (ANS) modulation, especially sympathovagal balance. This work aims to present concept of a personalizable HR model and an in silico system to identify the HR regulation parameters and subsequently capture residual heart beat-to-beat variations from individual psychophysiological recordings in humans. The model encompasses respiratory sinus arrhythmia (RSA) and baroreflex mechanisms, and uses respiration and blood pressure signals and the time instances of R peaks from an electrocardiogram as inputs. The system extracts the residual displacements of the modeled R peaks relative to the real R peaks. Three components - tonic, spontaneous, and 0.1 Hz changes - can be derived from these R peak residual displacements and can, therefore, enhance HRV analysis beyond RSA and baroreflex. Our model-based concept suggests that these residuals are not merely modeling errors. The proposed method could help to investigate additional neural regulation impulses from the higher-order brain and other influences.