State Anxiety and Nonlinear Dynamics of Heart Rate Variability in Students

Dimitriy A. Dimitriev, Elena V. Saperova, Aleksey D. Dimitriev, Martin Gerbert Frasch
2016 PLoS ONE  
Objectives Clinical and experimental research studies have demonstrated that the emotional experience of anxiety impairs heart rate variability (HRV) in humans. The present study investigated whether changes in state anxiety (SA) can also modulate nonlinear dynamics of heart rate. Methods A group of 96 students volunteered to participate in the study. For each student, two 5-minute recordings of beat intervals (RR) were performed: one during a rest period and one just before a university
more » ... tion, which was assumed to be a real-life stressor. Nonlinear analysis of HRV was performed. The Spielberger's State-Trait Anxiety Inventory was used to assess the level of SA. Results Before adjusting for heart rate, a Wilcoxon matched pairs test showed significant decreases in Poincaré plot measures, entropy, largest Lyapunov exponent (LLE), and pointwise correlation dimension (PD2), and an increase in the short-term fractal-like scaling exponent of detrended fluctuation analysis (α1) during the exam session, compared with the rest period. A Pearson analysis indicated significant negative correlations between the dynamics of SA and Poincaré plot axes ratio (SD1/SD2), and between changes in SA and changes in entropy measures. A strong negative correlation was found between the dynamics of SA and LLE. A significant positive correlation was found between the dynamics of SA and α1. The decreases in Poincaré plot measures (SD1, complex correlation measure), entropy measures, and LLE were still significant after adjusting for heart rate. Corrected α1 was increased during the exam session. As before, the dynamics of adjusted LLE was significantly correlated with the dynamics of SA. The qualitative increase in SA during academic examination was related to the decrease in the complexity and size of the Poincaré plot through a reduction of both the interbeat interval and its variation. Anxiety is a negative emotional response to threatening circumstances [1] . State anxiety (SA) can be conceptualized as "a state in which an individual is unable to instigate a clear pattern of behavior to remove or alter the event/object/interpretation that is threatening an existing goal" [2] . The neural organization of anxiety spans multiple levels of the brain, from the complex visceral and somatic integration of the limbic system, to the elementary adaptive activity of the brainstem [3] . Anxiety is associated with elevated high blood pressure [4], increased heart rate (HR) [5] and an enhanced respiratory rate [6]. A key system, involved in the generation of this physiological arousal is the autonomic nervous system (ANS) [7] . The ANS responds both to central stimuli and to activation of reflex sensory inputs [8] . The simple reciprocal concept of sympathovagal balance has been the keystone of ANS physiology for many years [9] . Reciprocity is true for many autonomic reflexes, such as the baroreflex [10] or orthostatic stress [11] . In contrast to homeostatic sensory inputs, however, descending influences from rostral brain structures can evoke different patterns of autonomic reactivity, such as reciprocal, independent or coactive changes in the parasympathetic and sympathetic branches of the ANS [7]. The principal property of the ANS is variability. Autonomic outflow has been well established as intrinsically periodic [12, 13] . Some researchers [14, 15] proposed that brainstem autonomic circuits generate this rhythm (the central oscillator theory). This theory is supported by the observation that different oscillations are present in the firing of sympatheticrelated neurons of the medulla [16] . The alternative theory (the baroreflex feedback loop theory) postulates that a combination of time delays and feedback results in the oscillation of blood pressure and HR [17, 18] . Mathematical models of the ANS reveal nonlinear properties of these rhythms [19, 20] . Heart rate variability (HRV) is the difference between consecutive instantaneous beat intervals (RR) [21] . HRV may be an independent marker of cardiovascular health [22] and an indicator of ANS activity [23] . The HRV seems to show a beat-to-beat regulation to which the sympathetic and parasympathetic modulatory influences are probably opposite [24, 25] . The physiological background of HRV has been extensively described using statistical and linear spectral analysis methods [26]. A physiological system, that generates the RR time series data, has been conceptualized as a network of biological oscillators with non-linear proprieties [27] . Chaos is apparently a lawless behavior of a nonlinear system totally ruled by deterministic laws [28] . A healthy cardiovascular system is associated with HRV of a chaotic nature; this chaotic nature reflects adaptability, which can be defined as the capacity to respond to unpredictable stimuli [29] . Consequently, nonlinear behavior would indicate greater flexibility and smaller predictability than a linear behavior [30] . Complex temporal patterns of physiological signals can result from interaction between nonlinear oscillatory systems, including those demonstrating chaotic behavior [30] . Different nonlinear measures of HRV quantify different features of nonlinear dynamics of HR. Lyapunov exponents and entropy rates are measures of the dynamics on an attractor. The correlation dimension describes the complex structure of the attractor approximating the State Anxiety and Nonlinear Heart Rate Variability PLOS ONE | DOI:10.1371/journal.pone.0146131 January 25, 2016 2 / 22 fractal dimension. The Poincaré plot describes the evolution of a system. Detrended fluctuation analysis (DFA) quantifies the fractal correlation properties in physiological time series. By combining different nonlinear measures, different aspects of the underlying physiological patterns may be captured [19, 20, 27, 30] . The Poincaré plot is a scatterplot in which current R-R is plotted as a function of previous interval [31] . Poincaré plot analysis is based on a technique from nonlinear dynamics and provides detailed beat-to-beat information on the activity of the sinus node [31] . Analysis of the Poincaré plot can be used to not only to classify the signal into one of various classes (e.g. torpedo, butterfly, parabola, or comet) but also to fit an ellipse, which enables quantification of the Poincaré map [32] . Application of this method includes measurement of autonomic modulation, or randomness, of HR in physiological and clinical studies [32, 33, 34] . Anxiety is associated with a prominent reduction in the standard deviation of the Poincaré plot perpendicular to the line of identity (SD1) [35, 36] . Karmakar et al. [37] proposed a novel descriptor, the Complex Correlation measure (CCM), to quantify the temporal aspect of the Poincaré plot. In contrast to SD1 and dispersion along the line of identity (SD2), this measure incorporates point-to-point variation in the signal. In time series analysis, time irreversibility refers to the lack of invariance of the statistical properties of a signal under the operation of time reversal [38] . Asymmetric patterns (i.e., those with the ascending side shorter than the descending side or vice versa) suggest irreversibility, but irreversibility might not imply the presence of asymmetrical patterns [39] . Asymmetry is present in physiological systems as it is an essential property of a non-equilibrium system [40] . A visible and statistically highly significant asymmetry has been shown in the Poincaré plot [41] . Porta et al. [39] examined the asymmetry of a Poincaré plot and showed an interrelationship between time irreversibility, pattern asymmetry, and nonlinear dynamics. Recent studies indicate that simple irreversibility indexes are sensitive to autonomic changes during active orthostasis [42] and head-up tilt [39] . Some studies utilized the Poincaré plot in the case of university examinations [43], mental effort [44], and anxiety disorders [35] , but the utility of irreversibility indexes and complex correlation measure for anxiety research have not been well defined. Fishman et al. [45] pioneered an innovative method of temporal Poincaré variability (TPV), which is a novel analysis to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. Two measures of the Poincaré plot are proposed. The first, called time-delayed TPV (TPVTD) is the measure of the similarity of an interval to its successor. TPVTD is equivalent to SD1; and hence we excluded this method from consideration. The second measure is called long-term TPV (TPVA) and is calculated using the distance from the center of mass to the origin. Another approach to the nonlinear analysis of HRV is quantification of complexity. The most commonly used non-linear complexity measures are fractal dimensions of various kinds, and measures based on entropy [46] . Entropy is the measure of system randomness and predictability, with greater entropy often associated with more randomness and less system order [46] . The concept of entropy, as it applies to signals such as RR intervals, is to quantify the repetition of patterns in that signal [47] . Pincus [48] developed approximate entropy (ApEn) as a measure of system complexity. ApEn (m,r,N) is approximately the negative natural logarithm of the conditional probability that a dataset of length N, having repeated itself within a tolerance r for m points, will also repeat itself for m + 1 points. Reduced ApEn values, indicating large predictability and less complexity in HR dynamics, have been reported in patients with congestive heart failure [49] and schizophrenia [50] . In addition, ApEn increases during exercise have been reported [51] . Cholinergic blockade with atropine does not significantly impact ApEn [52] . State Anxiety and Nonlinear Heart Rate Variability PLOS ONE |
doi:10.1371/journal.pone.0146131 pmid:26807793 pmcid:PMC4726749 fatcat:6ka5ybgq65hr5p4xsiqrlaiwha