Physiological modeling of autonomic regulation of cardiac system under graded exercise

Abstract

Background and Objective

: Dysfunction of the autonomic nervous system (ANS) plays a critical role in the progression and assessment of cardiovascular diseases, neurological disorders, and various other pathologies. Therefore, a quantitative assessment of ANS function is vital for personalized medicine in these diseases. However, direct measurements of ANS activity can be costly and invasive, prompting researchers to adopt indirect methods for quantitative evaluation. These methods typically involve mathematical techniques, such as statistical analysis and mathematical modeling, to interpret cardiovascular fluctuations in response to external stimuli.The purpose of this study is to develop a non-invasive mathematical method that quantitatively assesses ANS function during graded exercise.

Methods:

In this study, we present a physiological mathematical model for autonomic regulation of the cardiac system under graded exercise, which recognizes the crucial role of the ANS in controlling heart rate during physical activity. The model utilizes the metabolic equivalent of walking as the input and heart rate as the output, with model parameters serving as quantitative measures of personalized ANS function. Experimental data were collected from groups with different health statuses and genders. Mann–Whitney U non-parametric tests were conducted on the model parameters to assess performance between individuals who frequently engage in aerobic exercise (15 participants, aerobic exercise frequency of more than 4 times/week) and those who barely exercise (15 participants, aerobic exercise frequency of 1 time per week or less), as well as between male and female participants.

Results:

The experimental results indicate that our model effectively quantitatively assesses ANS function across groups with different health statuses and genders (P $<$ 0.05). Additionally, the model provides precise estimations of heart rate, yielding a Root Mean Square Error of 2.79 beats per minute, a Mean Absolute Error of 2.18 beats per minute, and an R-squared value of 0.93.

Conclusion:

Our findings suggest that the proposed physiological mathematical model offers a non-invasive and user-friendly tool for measuring ANS function and monitoring cardiovascular health. This approach is feasible for home application, thereby reducing the need for professional supervision, and supports the early detection and personalized management of cardiovascular diseases. As a result, it enhances clinical decision-making and improves patient outcomes.