Li, B., Li, X., Lu, X., Kang, R., Tian, Z., & Ling, F. (2024). Utilizing entropy to systematically quantify the resting-condition baroreflex regulation function. Complexity, 2024, 5514002. https://doi.org/10.1155/2024/5514002
This study aims to develop a new method for quantifying the resting-condition baroreflex regulation function (BRF) using easily measurable physiological indexes and to explore the relationship between BRF and the interactions among different physiological mechanisms.
The researchers propose a new index called physiological entropy (PhysioEnt) to quantify the fluctuations of four physiological indexes related to baroreflex function: systolic blood pressure (SBP), heart rate (HR), heart rate variability (HRV), and baroreflex sensitivity (BRS). They utilize the principle of maximum entropy (MaxEnt) to construct a model that estimates the joint probability distribution of these four indexes, considering their interactions. The model is then used to calculate PhysioEnt for each index, and the relative contributions of different model components (representing physiological processes or organs/tissues) are analyzed. The proposed method is applied to two open-source datasets: the Eurobavar dataset and the Jena dataset.
The proposed method, utilizing PhysioEnt and MaxEnt modeling, offers a promising approach to quantify resting-condition BRF based on easily measurable physiological indexes. The findings suggest that BRF is significantly influenced by the interactions among different physiological processes and exhibits distinct demographic features.
This research provides a novel and practical method for BRF assessment, which could potentially aid in the diagnosis, treatment, and healthcare of related diseases. The study also sheds light on the complex interplay of physiological mechanisms involved in baroreflex regulation.
The study primarily focuses on the resting-condition BRF and utilizes data from specific datasets. Further research is needed to validate the method in different physiological conditions and diverse populations. Future studies could also explore the application of the proposed method in clinical settings for disease diagnosis and personalized healthcare.
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by Bo-Yuan Li, ... at arxiv.org 10-07-2024
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