Key result
Personalizable computational model captures residual beat-to-beat variations beyond respiratory sinus arrhythmia and baroreflex.
Why the study?
Heart rate and HRV reflect autonomous nervous system modulation, prompting the development of a personalizable model to capture residual beat-to-beat variations beyond respiratory sinus arrhythmia and baroreflex.
This computational modeling approach provides a novel method to investigate heart rate variability and neural regulation impulses from higher-order brain structures by analyzing residual R peak displacements.
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.
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Baranauskas et al. (2023) studied Heart rate regulation modeling. Computational heart rate regulation model was evaluated on Residual heart beat-to-beat variations beyond RSA and baroreflex. The proposed personalizable computational model captures residual beat-to-beat variations beyond respiratory sinus arrhythmia and baroreflex, extracting tonic, spontaneous, and 0.1 Hz components.
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