Abstract
The powers of the low frequency (LF) and high frequency (HF) components of heart rate variability (HRV) have become the de facto standard metrics in the assessment of the stress response, and the related activities of the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). However, the widely adopted physiological interpretations of the LF and HF components in SNS /PNS balance are now questioned, which puts under serious scrutiny stress assessments which employ the LF and HF components. To avoid these controversies, we here introduce the novel Classification Angle (ClassA) framework, which yields a family of metrics which quantify cardiac dynamics in three-dimensions. This is achieved using a finite-difference plot of HRV, which displays successive rates of change of HRV, and is demonstrated to provide sufficient degrees of freedom to determine cardiac deceleration and/or acceleration. The robustness and accuracy of the novel ClassA framework is verified using HRV signals from ten males, recorded during standardized stress tests, consisting of rest, mental arithmetic, meditation, exercise and further meditation. Comparative statistical testing demonstrates that unlike the existing LF-HF metrics, the ClassA metrics are capable of distinguishing both the physical and mental stress epochs from the epochs of no stress, with statistical significance (Bonferroni corrected p-value ≤ 0.025); HF was able to distinguish physical stress from no stress, but was not able to identify mental stress. The ClassA results also indicated that at moderate levels of stress, the extent of parasympathetic withdrawal was greater than the extent of sympathetic activation. Finally, the analyses and the experimental results provide conclusive evidence that the proposed nonlinear approach to quantify cardiac activity from HRV resolves three critical obstacles to current HRV stress assessments: (i) it is not based on controversial assumptions of balance between the LF and HF powers; (ii) its temporal resolution when estimating parasympathetic dominance is as little as 10 s of HRV data, while only 60 s to estimate sympathetic dominance; (iii) unlike LF and HF analyses, the ClassA framework does not require the prohibitive assumption of signal stationarity. The ClassA framework is unique in offering HRV based stress analysis in three-dimensions.
Highlights
The endeavor to objectively discern states of stress from rest using only heart rate variability (HRV) has long been a subject of research
It is well established that the responses to stress are not exclusively sympathetic; stress reactions driven by the parasympathetic nervous system (PNS) include the production of tears and the emptying of the bladder (Berntson et al, 1991; Avnon et al, 2004)
Median aLF reached a peak at 0.0011 ms2 during the arithmetic test, and was lowest during exercise, at 0.00044 ms2, whilst Median aHF peaked during rest, at 0.00036 ms2, and was lowest at 0.00012 ms2, during exercise
Summary
The endeavor to objectively discern states of stress from rest using only heart rate variability (HRV) has long been a subject of research. Attempts to quantitatively identify stress from physiological signals have included temporal (Castrillión et al, 2017), spectral (Montano et al, 1994), and nonlinear (Vuksanovic and Gal, 2007) measures, conventional algorithms have not been able to achieve an accurate and precise discernment of stress states from rest. This is a reflection of the complexity of the human stress response, for which the causes are manifold, and the manifestations are yet to be fully understood. Other traditional measures of stress include temporal methods, such as the standard deviation of beat-to-beat intervals (SDNN), the spectral components of HRV, and nonlinear signal measures (Malik et al, 1996)
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