Enhancing cardiovascular monitoring: a non-linear model for characterizing RR interval fluctuations in exercise and recovery
Abstract This work aimed to develop and validate a novel non-linear model to characterize RR interval (RRi) time-dependent fluctuations throughout a rest-exercise-recovery protocol, offering a more precise and physiologically relevant representation of cardiac autonomic responses than traditional HR...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-93654-6 |
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| Summary: | Abstract This work aimed to develop and validate a novel non-linear model to characterize RR interval (RRi) time-dependent fluctuations throughout a rest-exercise-recovery protocol, offering a more precise and physiologically relevant representation of cardiac autonomic responses than traditional HRV metrics or linear approaches. Using data from a cohort of 272 elderly participants, the model employs logistic functions to capture the non-stationary and transient nature of RRi time-dependent fluctuations, with parameter estimation achieved via Hamiltonian Monte Carlo. Sobol sensitivity analysis identified baseline RRi (α) and recovery proportion (c) as the primary drivers of variability, underscoring their critical roles in autonomic regulation and resilience. Validation against real-world RRi data demonstrated robust model performance (R 2 = 0.868, CI95%[0.834, 0.895] and Root Mean Square Error [RMSE] = 32.6 ms, CI95%[30.01, 35.77]), accurately reflecting autonomic recovery and exercise-induced fluctuations. By advancing real-time cardiovascular assessments, this framework holds significant potential for clinical applications in rehabilitation and cardiovascular monitoring in athletic contexts to optimize performance and recovery. These findings highlight the model’s ability to provide precise, physiologically relevant assessments of autonomic function, paving the way for its use in personalized health monitoring and performance optimization across diverse populations. |
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| ISSN: | 2045-2322 |