Abstract

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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* English language revised and improved.

* https://doi.org/10.5281/zenodo.7765459

Details

Title
Model-based concept to extract heart beat-to-beat variations beyond respiratory arrhythmia and baroreflex
Author
Baranauskas, Mindaugas; Stanikūnas, Rytis; Kaniušas, Eugenijus; Lukoševičius, Arūnas
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2023
Publication date
Dec 23, 2023
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2905061400
Copyright
© 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.