Combined heart rate variability and dynamic measures for quantitatively characterizing the cardiac stress status during cycling exercise

• We seek for different ways of measuring cardiac stress that occurs when exercising.
• Both the SDNN and fractal exponent α produced a decrease during the cycling exercise.
• Both the SDNN and fractal exponent α were negatively correlated with the Borg index.
• A time-varying index CSM was devised for quantitatively measuring the cardiac stress.
• The study may be used in sports medicine or stress tests for heart stress monitoring.

In this study, we aimed to seek for different ways of measuring cardiac stress in terms of heart rate variability (HRV) and heart rate (HR) dynamics, and to develop a novel index that can effectively summarize the information reflected by these measures to continuously and quantitatively characterize the cardiac stress status during physical exercise. Standard deviation, spectral measure of HRV as well as a nonlinear detrended fluctuation analysis (DFA) based fractal-like behavior measure of HR dynamics were all evaluated on the RR time series derived from windowed electrocardiogram (ECG) data for the subjects undergoing cycling exercise. We recruited eleven young healthy subjects in our tests. Each subject was asked to maintain a fixed speed under a constant load during the pedaling test. We obtained the running estimates of the standard deviation of the normal-to-normal interval (SDNN), the high-fidelity power spectral density (PSD) of HRV, and the DFA scaling exponent α, respectively. A trend analysis and a multivariate linear regression analysis of these measures were then performed. Numerical experimental results produced by our analyses showed that a decrease in both SDNN and α was seen during the cycling exercise, while there was no significant correlation between the standard lower frequency to higher frequency (LF-to-HF) spectral power ratio of HRV and the exercise intensity. In addition, while the SDNN and α were both negatively correlated with the Borg rating of perceived exertion (RPE) scale value, it seemed that the LF-to-HF power ratio might not have substantial impact on the Borg value, suggesting that the SDNN and α may be further used as features to detect the cardiac stress status during the physical exercise. We further approached this detection problem by applying a linear discriminant analysis (LDA) to both feature candidates for the task of cardiac stress stratification. As a result, a time-varying parameter, referred to as the cardiac stress measure (CSM), is developed for quantitatively on-line measuring and stratifying cardiac stress status.