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Complexity and time asymmetry of heart rate variability are altered in acute mental stress

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Institute of Physics and Engineering in Medicine Physiol. Meas. 35 (2014) 1319–1334

Physiological Measurement

doi:10.1088/0967-3334/35/7/1319

Complexity and time asymmetry of heart rate variability are altered in acute mental stress Z Visnovcova 1 , M Mestanik 1 , M Javorka 1 , D Mokra 1 , M Gala 2 , A Jurko 3 , A Calkovska 1 and I Tonhajzerova 1 1 Department of Physiology, Jessenius Faculty of Medicine, Comenius University, Martin, Slovak Republic 2 Department of Electromagnetic and Biomedical Engineering, University of Zilina, Zilina, Slovak Republic 3 Pediatric Cardiology, Jessenius Faculty of Medicine, Comenius University, Martin, Slovak Republic

E-mail: [email protected] Received 2 October 2013, revised 13 March 2014 Accepted for publication 17 April 2014 Published 22 May 2014 Abstract

We aimed to study the complexity and time asymmetry of short-term heart rate variability (HRV) as an index of complex neurocardiac control in response to stress using symbolic dynamics and time irreversibility methods. ECG was recorded at rest and during and after two stressors (Stroop, arithmetic test) in 70 healthy students. Symbolic dynamics parameters (NUPI, NCI, 0V%, 1V%, 2LV%, 2UV%), and time irreversibility indices (P%, G%, E) were evaluated. Additionally, HRV magnitude was quantified by linear parameters: spectral powers in low (LF) and high frequency (HF) bands. Our results showed a reduction of HRV complexity in stress (lower NUPI with both stressors, lower NCI with Stroop). Pattern classification analysis revealed significantly higher 0V% and lower 2LV% with both stressors, indicating a shift in sympathovagal balance, and significantly higher 1V% and lower 2UV% with Stroop. An unexpected result was found in time irreversibility: significantly lower G% and E with both stressors, P% index significantly declined only with arithmetic test. Linear HRV analysis confirmed vagal withdrawal (lower HF) with both stressors; LF significantly increased with Stroop and decreased with arithmetic test. Correlation analysis revealed no significant associations between symbolic dynamics and time irreversibility. Concluding, symbolic dynamics and time irreversibility could provide independent information related to alterations of neurocardiac control integrity in stress-related disease. Keywords: acute stress, complexity, heart rate variability, symbolic dynamics, time irreversibility 0967-3334/14/071319+16$33.00

© 2014 Institute of Physics and Engineering in Medicine Printed in the UK

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1. Introduction Spontaneous short-term oscillations of heart rate—heart rate variability (HRV)—reflect the complex cardiac autonomic regulation indicating a healthy and adaptive organism. However, the heart rate is controlled by a complex regulatory system. Thus, the reduction of cardiac control network complexity indicates insufficient adaptation to different requirements, leading to higher risk of cardiac events (Goldberger et al 2002, Bornas et al 2006, B¨ar et al 2007). Moreover, the exaggerated/lower cardiovascular reactivity to mental stress and delayed recovery time could predict future cardiovascular complications (Chida and Steptoe 2010, Lovallo 2011). The HRV is routinely analysed by a linear method quantifying predominantly the magnitude of heart rate fluctuations, but does not characterize the complex dynamics of heart rate modulation providing additional information on neurocardiac integrity (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996, Bettermann et al 2001). Thus, nonlinear methods quantifying the qualitative features of complex dynamics are increasingly used in HRV analysis (Porta et al 2008, Javorka et al 2009, Voss et al 2009, Hou et al 2010). Symbolic dynamics is a suitable method for the quantification of cardiac time series complexity independent of its magnitude (Guzzetti et al 2005, Voss et al 2009). Recent studies have indicated that symbolic dynamics analysis is superior to conventional spectral indices due to its sensitivity to sympathetically mediated heart rate fluctuations (Porta et al 2006). The analysis of time asymmetry—as the phenomenon specific for nonequilibrium systems (Hou et al 2010)—checks the invariance of the statistical properties of a time series after time reversal potentially detecting a specific class of heart rate nonlinear dynamics (Porta et al 2008). Although both symbolic dynamics and time irreversibility methods have been applied to HRV analysis in healthy young subjects during physical stressors—passive and active orthostasis (Casali et al 2008, De La Cruz Torres and Narajno Orellana 2010)—and in diseases such as chronic heart failure, postinfarction patients, diabetes mellitus type 1 or depression (Baumert et al 2009, Porta et al 2009, Guzik et al 2012, 2010, Tonhajzerova et al 2012, 2010), there are no previous studies related to acute mental stress. We aimed to study HRV complexity and time asymmetry using symbolic dynamics and time irreversibility analysis in response to acute mental stress in otherwise healthy young students. In addition, HRV magnitude was assessed by linear analysis. To the best of our knowledge, this is the first study related to symbolic dynamics and time irreversibility with cognitive stressors in healthy young students. 2. Methods 2.1. Subjects

The studied group consisted of 70 healthy young students attending the fifth year at Jessenius Faculty of Medicine (39 women, average age 23.08 ± 0.17 years). They were normotensive, nonobese (BMI 22.1 ± 0.3, WHR 0.8 ± 0.01) and were taking no medication for the duration of the study. The following exclusion criteria were used when enrolling the students: a history of respiratory, endocrinological, cardiovascular, infectious, mental or other diseases potentially influencing HRV (including obesity, underweight, overweight, alcohol or drug abuse). Smokers were excluded from this study. All students were instructed not to use substances which affect the cardiovascular system (caffeine, alcohol) for at least 12 h before the recording. Importantly, because hormonal changes during the menstrual cycle can affect the cardiac autonomic regulation (Hirshoren et al 2002), females were included in the proliferative phase. 1320

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Figure 1. Stress protocol.

This study was approved by the Ethics Committee of Jessenius Faculty of Medicine, in accordance with the Declaration of Helsinki. All students were carefully instructed about the study protocol and they gave their informed consent to prior to examination. 2.2. Protocol

All students were examined under standard conditions: a quiet room with a standard temperature (23 ◦ C) and humidity (45%–55%), minimization of stimuli, in the morning between 9.00 am and 12.30 pm after normal breakfast 2 h prior to the examination. The students were asked to empty their bowels and bladders before the examination. After anthropometric measurement (InBody J10, Biospace, Korea) they were instructed to sit comfortably in a special armchair and not to speak or move unless necessary. After 15 min required for heart rate stabilization and for exclusion of a potential stress effect the students remained in a sitting position. Then, continuous recording of RR interval was performed in the following order: baseline (T1), Stroop test (T2), rest after stress–recovery (T3), mental arithmetic test (T4) and recovery phase (T5, figure 1) using VarCorPF8 (Dimea, Czech Republic) with a sampling frequency of 1000 Hz. The applied system was developed and validated according to methodological recommendations of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996), (Salinger and Gvozdziewicz 2008). The time of each phase was 6 min, recommended by Task Force (1996) for short-term HRV analysis, enabling evaluation of the sympathovagal balance changes developing immediately after the onset and the end of stress stimulus. The stress protocol was modified according to studies concerning stress assessment (Crowley et al 2011). 2.3. Mental stress

The Stroop colour–word interference test was selected as the stressor because it has been shown to be a reliable and valid method of inducing a moderate level of transitory physiological modifications (Dishon-Berkovits and Algom 2000, TeixeiraSilva et al 2004, Willmann et al 2012). Students read the colours (green, yellow, orange, red, blue, purple) of the words displayed on the screen, which are congruent or incongruent with the written word (figure 2). A metronome was applied as a distracting sound during this test.

2.3.1. Stroop colour–word test.

The mental arithmetic test is a standard stressor with moderate intensity used in physiology for detection of changes in function of the autonomic nervous system (Schneider et al 2003). The students calculated three-digit numbers displayed in different random places on the screen into one-digit numbers. Subsequently, participants decided that the final result was even or odd by pushing the keyboard arrow (left—odd, right—even) (figure 3). The reaction time and right/wrong responses were evaluated. As previously, the metronome was applied as a distracting sound.

2.3.2. Mental arithmetic test.

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Figure 2. Stroop colour–word test.

Figure 3. Mental arithmetic test.

2.4. Data analysis

The HRV analysis was focused on linear and nonlinear approaches with a selection of suitable parameters recommended for short-term recordings (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996, Porta et al 2001, 2002, Hou et al 2010, Vandeput et al 2010). These selected parameters show adequate reproducibility, which underlines their suitability for the application (Maestri et al 2007, La Fountaine et al 2010, Klintworth et al 2012). First, the recordings were carefully visually checked and the rare artefacts were removed manually as required for exact analysis of 300 consecutive RR intervals. 2.5. Nonlinear analysis

The concept of symbolic dynamics is based on coarse-graining of the time series dynamics. Time series are transformed to symbol sequences with a certain given alphabet (Cover and Thomas 1991, Porta et al 2007). The number of symbols depends on recording length. The signal with 300 RR intervals was transformed by four letters (0, 1, 2, 3) of the alphabet to classify the dynamic changes of series, then these symbols were used to generate three basic indexes of symbolic time series (Porta et al 2001, 2007, Voss et al 2012).

2.5.1. Symbolic dynamics.

(1) The normalized index of complexity (NCI) evaluated the amount of information carried by the Lth sample when the previous L − 1 samples are known (Porta et al 2001, 2007). NCI provides information about the complexity of the pattern distribution. Its range is from zero (maximum regularity) to unity (maximum complexity). The higher value of NCI, the more complex and less regular the time series are. (2) The normalized unpredictability index (NUPI) provides information about the unpredictability of the variances in the time series. It ranges from zero to unity. An NUPI decline to zero indicates fully predictable signals. In contrast, a NUPI increase refers to a more unpredictable time series. (3) Pattern classification. All patterns with length L = 3 were grouped into four families according to the type and number of variations between consecutive symbols (Porta et al 1322

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2007). The pattern families were (1) patterns with no variation (0 V, all three symbols are equal), (2) patterns with one variation (1 V, two consecutive symbols are equal, the last is different), (3) patterns with two unlike variations (2UV, three symbols from a descending or an ascending gradient), and (4) patterns with two unlike variations (2LV, three symbols from a valley or a peak). Indices 0V%, 1V%, 2UV%, and 2LV% indicated the rate of occurrence of these patterns (Porta et al 2006, 2007). 2.5.2. Time irreversibility. The principle of the calculation of time irreversibility indices is based on the difference between two consecutive RR intervals—d. The most common used parameters of time irreversibility were evaluated. Porta’s index (P%) evaluates the percentage of negative d (d−) with respect to the total number of d = 0 (Porta et al 2008, Hou et al 2010):

P% =

N(d − ) 100 N(d = 0)

(1)

N(d−) denotes the number of negative d and N(d = 0) indicates the number of all nonzero d. Guzik’s index (G%) evaluates the percentage of the cumulative square of positive d (d+) with respect to the cumulative square of d (Guzik et al 2006, Porta et al 2008, Hou et al 2010): N(d + ) d(i)+2 100. (2) G% = i=1 N(d) d(i)2 i=1 In contrast to P%, G% considers the magnitude of the differences between two RR intervals. The range of P% and G% indexes is from 0 to 100. The number 50 represents a ‘cutoff point’ for significant changes in these parameters. P% higher than 50 means that the negative changes (d−) are larger than the positive (d+) (i.e. bradycardic runs are shorter than tachycardic ones). G% higher than 50 means that the average magnitude |d+| is larger than the negative one. The values below 50 evoke the opposite reactions. Ehlers’ index (E) evaluates the skewness of the distribution of d (Ehlers et al 1998, Porta et al 2008): N(d) d(i)3 i=1 E= (3) 3/2 . N(d) 2 d(i) i=1 The limiting value of this index represents number 0. Ehlers’ index higher than 0 indicates that the distribution of d is skewed towards positive values and vice versa. 2.6. Linear (spectral) analysis

Before the spectral analysis, the slower oscillations and trends were eliminated using the detrending procedure of Tarvainen et al (2002) and time series were resampled (resampling frequency of 2 Hz) to obtain equidistant time series using cubic spline interpolation. The mean power spectrum of the analysed segment was computed by fast Fourier transform (using a window length of 256 samples), and spectral powers in the appropriate frequency bands were obtained by integration in two frequency bands that have been associated with different physiological rhythms: low frequency band (LF: 0.04–0.15 Hz), which is determined by both the parasympathetic and sympathetic activity and 0.1 Hz oscillations, which are mediated mostly by baroreflex activity; and high frequency band (HF: 0.15–0.4 Hz), reflecting mainly 1323

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respiratory sinus arrhythmia (RSA) as an index of cardiac vagal control. In addition, mean RR interval (ms) and SD-RR (ms) as an index of overall HRV were calculated. The breathing parameters were assessed from linear HRV analysis. The respiratory dominant HF frequency (Hz) was determined from location of the largest peak in a running average of the power spectrum obtained from signals (Bailon et al 2006), and the respiratory rate (breaths min−1) was assessed on the basis of respiratory-related HF frequency. 2.7. Statistics

Statistical analysis was performed using statistical software SYSTAT 10 (SSI, Richmond, CA, USA). The Lilliefors test was used for analysis of Gaussian or non-Gaussian distribution. Because absolute values of linear HRV analysis indices differ greatly among individuals, frequency-domain HRV parameters were logarithmically transformed for statistical testing. The paired t-test was used to test the indices with Gaussian distribution between baseline and stress phase, between stress and recovery phase and between baseline and recovery phase in the same population. The Wilcoxon signed rank nonparametric test was used for variables with non-Gaussian distribution. Furthermore, we investigated the relationship between the different HRV indices using Spearman correlation coefficients. The results are presented as mean ± SEM for Gaussian distribution and as medians (interquartile ranges) for non-Gaussian distribution. A value of p < 0.05 (two tailed) was considered statistically significant. 3. Results 3.1. Stroop test effect on heart rate variability 3.1.1. During stress—reactivity. The symbolic dynamics parameters—NCI, NUPI, 2LV%, 2UV%—were significantly lower during the Stroop test (T2) (p < 0.001 for all) compared to baseline (T1). In contrast, 0V% and 1V% significantly increase in response to stress (T2) (p < 0.001, p = 0.042, respectively). Time irreversibility indices—G% and E—were significantly decreased during test (T2) compared to baseline (T1) (p = 0.013, p = 0.002, respectively). Linear analysis revealed significantly shortened mean RR interval, lower log SD-RR and log HF-HRV and higher log LF-HRV in response to stress (T2) (p < 0.001, p < 0.001, p < 0.001, p = 0.027, respectively). No significant changes were found in P% index. Additionally, breathing parameters (respiratory HF-frequency and respiratory rate) were significantly higher with Stroop test (T2) compared to baseline (T1) (p < 0.001).

Symbolic dynamics and time irreversibility indices—NCI, NUPI, 2LV%, 2UV%, G% and E—were significantly higher (p < 0.001 for all) and parameters 0V%, 1V% significantly lower in T3 compared to acute stress (T2) (p < 0.001, p = 0.005, respectively). The mean RR interval was significantly prolonged, and log SD-RR and log HF-HRV significantly increased and log LF-HRV and breathing parameters significantly decreased in T3 compared to stress (T2) (p < 0.001, p < 0.001, p < 0.001, p = 0.032, p < 0.001, respectively). Index P% was without significant differences. The majority of HRV parameters showing significant change in response to stress—NCI, NUPI, 1V%, 2LV%, 2UV%, G%, E, mean RR interval, log SD-RR, log LF-HRV, log HF-HRV, breathing parameters—were without significant changes between baseline (T1) and recovery (T3) phases, indicating their return to initial values before stress. In contrast, the symbolic dynamics 0V% index was significantly higher in recovery compared to the baseline period (p = 0.002). All results are summarized in table 1. 3.1.2. After stress—recovery.

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Baseline (T1)

NCI NUPI 0V% 1V% 2LV% 2UV%

0.75 0.50 14.94 41.86 24.30 18.89

± ± ± ± ± ±

0.02 0.02 1.18 0.62 0.92 1.29

Stroop task (T2)

0.65 0.34 27.14 43.65 18.18 11.04

Recovery (T3)

Symbolic dynamics ± 0.01aaa 0.73 0.49 ± 0.02aaa ± 1.17aaa 16.98 ± 0.46a 41.42 22.90 ± 0.73aaa ± 0.75aaa 17.69

± ± ± ± ± ±

0.01bbb 0.02bbb 1.14bbb; cc 0.56bb 0.80bbb 1.18bbb

Arithmetic test (T4)

0.71 0.42 21.39 41.49 17.91 16.82

± ± ± ± ± ±

0.02 0.02aa; dd 1.02aaa;dd 0.60 0.81aaa;ddd 1.19

Recovery (T5)

0.74 0.49 16.60 40.95 23.57 18.53

± ± ± ± ± ±

0.01 0.02b 1.13bbb;c 0.75 0.79bbb 1.19

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Table 1. Effect of Stroop task and mental arithmetic test on the heart rate variability parameters.

Time irreversibility P% G% E

49.93 ± 0.53 52.04 ± 0.75 0.02 ± 0.004

49.24 ± 0.53 49.69 ± 0.92a 0.004 ± 0.004aa

50.22 ± 0.49 53.14 ± 0.82bbb 0.02 ± 0.005bbb

48.30 ± 0.38aa;dd 49.92 ± 0.59a;dd 0.003 ± 0.003aa;ddd

49.77 ± 0.53bb 51.89 ± 0.71bb 0.01 ± 0.003bb

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Linear analysis Mean RR interval (ms) log SD-RR (ms) Log LF-HRV (ms2) Log HF-HRV (ms2)

847.78 1.75 6.12 6.46

± ± ± ±

15.58 0.02 0.10 0.11

675.99 1.69 6.41 6.10

± ± ± ±

14.42aaa 0.19aaa 0.09a 0.12aaa

846.94 1.77 6.35 6.43

± ± ± ±

15.47bbb 0.19bbb 0.09b 0.10bbb

728.92 1.67 5.68 5.69

± ± ± ±

16.02aaa;ddd 0.19aaa; ddd 0.11aaa;ddd 0.12aaa;ddd

852.02 1.78 6.41 6.48

± ± ± ±

14.46bbb 0.13bbb 0.09bbb 0.09bbb

Respiratory parameters Respiratory rate (breaths min−1) Respiratory HF-frequency (Hz)

10 ± 2 0.17 ± 0.04

17 ± 3aaa 0.29 ± 0.06aaa

11 ± 2bbb 0.18 ± 0.04bbb

17 ± 2aaa;ddd 0.29 ± 0.05aaa;ddd

11 ± 3bbb 0.18 ± 0.05bbb

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Data are expressed as a mean ± SEM. All abbreviations are expressed in text. a Significant differences between baseline period and Stroop task (T1 versus T2) and between baseline phase and mental arithmetic test (T1 versus T4). ap < 0.05; aap < 0.01; aaap < 0.001. b Significant differences between Stroop task and recovery period (T2 versus T3) and between mental arithmetic test and recovery phase (T4 versus T5). bp < 0.05; bbp < 0.01; bbbp < 0.001. c Significant differences between baseline phase and recovery periods (T1 versus T3 and T1 versus T5). cp < 0.05; ccp < 0.01. d Significant differences between pre-test period and mental arithmetic test (T3 versusT4). ddp < 0.01; dddp < 0.001.

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3.2. Mental arithmetic test effect on heart rate variability

The symbolic dynamics and time irreversibility parameters—NUPI, 2LV%, P%, G%, E—were significantly lower during the mental arithmetic test (T4) compared to the baseline period (T1) (p = 0.004, p < 0.001, p = 0.003, p = 0.012, p = 0.004, respectively). Index 0V% was significantly higher during stress (T4) compared to T1 (p < 0.001). Linear analysis revealed significantly shortened mean RR interval and lower parameters log SD-RR, log HF-HRV, log LF-HRV, and higher breathing parameters in response to load (p < 0.001, for all). No significant changes were found in other parameters.

3.2.1. During stress—reactivity.

3.2.2. During stress—reactivity between pre-test period (T3) and mental arithmetic test (T4).

The symbolic dynamics and time irreversibility indices—NUPI, 2LV%, P%, G%, E—were significantly lower during the mental arithmetic test (T4) compared to the pre-test period (T3) (p = 0.004, p < 0.001, p = 0.002, p = 0.005, p < 0.001, respectively). Index 0V% was significantly higher during stress (T4) compared to T3 (p = 0.007). Indices of linear analysis—log SD-RR, log HF-HRV, log LF-HRV—were significantly lower and breathing parameters significantly higher in response to load compared to the pre-test period (T3) (p < 0.001, for all). Mean RR interval was significantly shortened during the stress test (p < 0.001). No significant changes were found in other parameters. Indices of symbolic dynamics and time irreversibility—NUPI, 2LV%, P%, G%, E— were significantly higher and 0V% significantly lower in recovery phase (T5) compared to stress (T4) (p = 0.014, p < 0.001, p = 0.007, p = 0.002, p = 0.002, p < 0.001, respectively). Parameters of linear analysis—log SD-RR, log HF-HRV, log LF-HRV— were significantly higher, breathing parameters significantly lower and mean RR interval significantly prolonged in recovery (T5) compared to stress (T4) (p < 0.001 for all). As previously, the HRV parameters showing significant change in response to arithmetic test—NUPI, 2LV%, P%, G%, E, mean RR interval, log SD-RR, log LF-HRV, log HF-HRV, breathing parameters—were without significant changes between baseline (T1) and recovery (T5) phases, indicating their return to initial values before stress. In contrast, symbolic dynamics 0V% was significantly higher in the recovery (T5) compared to the baseline period (T1) (p = 0.046). All the HRV indices—NUPI, 2LV%, 0V%, P%, G%, E, mean RR interval, log SR-RR, log HF-HRV, log LF-HRV, breathing parameters—were without significant changes between the pre-test period (T3) and recovery phase (T5). All results are summarized in table 1.

3.2.3. After stress—recovery.

We found close relationships between indices of symbolic dynamics (NCI, NUPI, 0V%, 1V%, 2LV%, 2UV%) during all periods of stress protocol: positive correlations between NCI and NUPI, NCI and 2LV%, NCI and 2UV%, NUPI and 2LV%, NUPI and 2UV%, 2LV% and 2UV%, 0V% and 1V% (r is in the range from 0.31 to 0.97 and from p = 0.047 to p < 0.001); negative correlations between NCI and 0V%, NCI and 1V%, NUPI and 0Vˇ %, NUPI and 1V%, 2LV% and 0V%, 2LV% and 1V%, 0V% and 2UV%, 1V% and 2UV% (r is in the range from −0.90 to −0.31 and from p < 0.001 to p = 0.047). In heart rate asymmetry indices, the analysis revealed a positive correlation between G% and E parameters in each phase of the stress protocol (r is in the range from 0.93 to 0.98; p < 0.001). Mutual relationships between P% and G% and P% and E were not confirmed. Moreover, the analysis showed negative correlations between log LF-HRV and log HFHRV in response to the Stroop test and after the end (recovery) (r is in the range from −0.66

3.2.4. Correlation analysis.

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to −0.45 and p from 0.0007 to 0.006). In contrast, positive correlations between log LF-HRV and log HF-HRV were found in the baseline period, during the mental arithmetic test and after the test (recovery; r is in the range from 0.46 to 0.66 and p from 0.006 to 0.0007). No significant correlations were found in other linear parameters. Statistically important positive correlations were found between baseline log HF-HRV and NCI (r = 0.35; p = 0.024), and between log HF-HRV and NUPI (r = 0.32; p = 0.044). A significant negative correlation was found between log HF-HRV and 0V% at rest (r = −0.34; p = 0.029). Additionally, correlation analysis revealed a tendency to positive correlation between log HF-HRV and 2LV% (r = 0.29, p = 0.06) at the baseline period. No significant correlations were found between symbolic dynamics and time irreversibility indices.

4. Discussion We assessed the effect of different cognitive stressors (Stroop, arithmetic test) on the neurocardiac complex regulation using short-term HRV analysis by linear and nonlinear methods in particular—symbolic dynamics and time irreversibility. Results from this study can be summarized as follows. We found that (1) symbolic dynamics and time irreversibility parameters were sufficiently sensitive to detect a sympathovagal shift in response to mental load, (2) time irreversibility indices showed different pattern during acute mental stressors, as we expected, and (3) linear HRV analysis confirmed vagal withdrawal indexed by log HF-HRV in response to acute stress, but the log LF-HRV distinct response between stressors probably depended on their intensity. To our best knowledge, this study is the first to describe features of the complex neurocardiac control using HRV symbolic dynamics and time irreversibility in response to mental stress. HRV analysis is a rapid, sensitive, noninvasive and reproducible tool to assess cardiac autonomic regulation (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996, Burger et al 2002). The mental arousal which follows a laboratory mental stress test produces a centrally induced shift of ANS balance towards the sympathetic activation associated with vagal withdrawal. Therefore, it can be used as an ideal ‘model’ to study the magnitude and complex dynamics of activated heart rate autonomic regulatory inputs. Linear (spectral analysis) of HRV allows us to isolate the faster HF respiratory-coupled oscillations as an index of cardiac vagal function (Berntson et al 1997, Martinm¨aki et al 2006). Our findings indicating vagal withdrawal indexed by log HF-HRV during stress, and vice versa after stress (recovery phase), are in agreement with the other studies (Tonhajzerova et al 2000, Taelman et al 2011, Niizeki and Saitoh 2012, Takamoto et al 2013) and extend them into a homogeneous sample of young healthy students. Moreover, the overall short-term HRV (indexed by log SD-RR) mediated mostly by parasympathetic regulatory inputs was reduced in response to stressors, with subsequent return to baseline values after they ended, indicating dynamic changes in vagal withdrawal/rebound (respectively). Interestingly, HRV linear analysis reveals distinct LF response to cognitive stressors: an increase with the Stroop test, and a decrease in the arithmetic test. As previously noted, the physiological mechanisms contributing to the LF fluctuations are controversial. Malliani et al (1994, 1991) reported that the LF component is determined (at least in part) by sympathetic activity in response to stress; however, recent studies referred to LF-HRV as a poor marker of sympathetic outflow (Moak et al 2007, Taelman et al 2011, Abukonna et al 2013). Therefore, we can speculate that LF could reflect the simultaneous dominance in sympathetic or vagal activation probably mediated through baroreflex, and these changes 1327

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might depend on the individual intensity of different used stressors to activate sympathovagal balance, as well (Tonhajzerova et al 2008, Jason et al 2009, Guasti et al 2010, Visnovcova et al 2013). However, linear HRV analysis describes the HRV magnitude, but does not characterize the complex dynamics of heart rate modulation that provides additional information on cardiac autonomic regulation integrity (Bettermann et al 2001). In accordance with other studies, which used various HRV complexity indices, such as Lyapunov exponent, sample entropy or approximate entropy (Schubert et al 2009, Melillo et al 2011), our findings revealed reduced HRV complexity with mental stress (lower NUPI in response to both stressors and lower NCI during the Stroop test) Additionally, significant positive correlations were found between NCI, NUPI and log HF-HRV in the baseline period. Therefore, we can hypothesize that vagal regulatory inputs could influence complex dynamics in heart rate regulation under resting conditions, in which the parasympathetic activity is dominant. It is important to our discussion that symbolic dynamics indices have been proposed as suitable to quantify the balance between sympathetic and parasympathetic cardiac modulation (Guzzetti et al 2005, Voss et al 2009). Several studies conclude that a cardiac sympathoexcitation associated with a vagal withdrawal induced by physical stressors (e.g. active or passive orthostatic test) causes a 0V% rise and a 2LV% decline, whereas opposite results are observed during manoeuvres associated with cardiovagal activation (Porta et al 2006, Tonhajzerova et al 2010, Perseguini et al 2011). Our study showed 0V% increase and 2LV% decline in response to both stressors. Thus, the 0V% index could reflect sympathetic activity, and 2LV% predominantly vagal regulatory effects (Porta et al 2006, 2001, Tonhajzerova et al 2010). A similar response pattern was observed for 1V% (as 0V%) and 2UV% (as 2LV%) indices, which showed close mutual relations (positive correlations between 0V% and 1V%, and 2LV% and 2UV%). However, 1V% and 2UV% significantly reacted only to the Stroop test, indicating lower sensitivity of these indices to detect a shift of sympathovagal balance with stress compared to 0V% and 2LV%. This could be explained by mathematical properties of the pattern classification: slower time scales reflecting potential cardiac sympathetic modulation are more characterized by three equal beats, i.e. stable patterns expressed by index 0V%, while faster fluctuations reflecting likely vagal activity are more characterized by three different beats, i.e. instable patterns indexed by the 2LV% index. Further explanation could include the distinct neurophysiological characteristics of the stressors: the Stroop colour–word test is based on the interference of two simultaneously acting stimuli with subsequent activation in different brain centres of left (verbal processing) and right (visuospatial processing) hemispheres. Moreover, the Stroop test is a more intensive stressor, with a verbal response, compared to the button-press nonverbal arithmetic test (Willmann et al 2012). Therefore, it could result in a greater response of physiological allostatic regulatory mechanisms than the arithmetic test, and it might be responsible for the different sensitivities of symbolic dynamics indices to mental stress. In addition, the reactive remnants as well as potential cognitive fixation on the first stressor (Stroop) might influence the second stressrelated response (arithmetic test). Importantly, the 0V% index was the only one of all the HRV indices that did not return to initial values, and full recovery has not been achieved. This finding could represent a novel response feature in heart rate complex dynamics after mental stress that has not yet been described. The sensitivity of symbolic dynamics 0V% to sympathetic cardiac activation (in contrast to linear measures) could be explained by the fact that these nonlinear metrics reflect HRV features fundamentally different from those reflected by linear parameters (Baumert et al 2009). In addition, this study found significantly negative correlation between 0V% and log HF-HRV (cardiovagal index). Thus, we suggest that 0V% can be considered as a 1328

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sensitive noninvasive parameter of cardiac sympathetic activity, and our results might reflect prolonged sympathetic excitation in the recovery phase. As one of the possible explanations, the neurophysiological differences in sympathetic (norepinephrine) and parasympathetic (acetylcholine) neurotransmitters’ time delay should be taken into consideration. Furthermore, we can speculate about the potential influence of perseverative cognitive processes such as the worry and rumination associated with sympathetic overactivity (Willmann et al 2012). This could be very important from a clinical point of view: the sympathetic hyperexcitation during stress and delayed time in the recovery phase are considered as critical pathomechanisms leading to cardiovascular complications (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996, Visnovcova et al 2013). It seems that the symbolic dynamics might represent a valid alternative to short-term HRV linear analysis for assessment of the cardiac autonomic modulation (Porta et al 2007), especially for the determination of the sympathetic activity, which is difficult to interpret on the basis of linear measures, reflecting mainly vagal activity. The nonlinear behaviour of the cardiac function includes the presence of asymmetric patterns (i.e. waveforms characterized by the upward side being shorter or longer than the downward side) (Porta et al 2008). Recent studies suggest that heart rate asymmetry is a result of sympathovagal balance control. Increased sympathetic activity during load does not occur at the same time as vagal withdrawal, implying that it tends to originate a time lag, which causes a time asymmetry of heart rate (Klintworth et al 2012). The heart rate asymmetric pattern in healthy subjects is characterized by bradycardic runs shorter than tachycardic ones (i.e. the heart rate decelerates more rapidly than it accelerates), indexed by values of P% (described a number of decrements or increments of length of RR intervals) and G% (expressed magnitude of differences between two consecutive RR peaks) larger than 50 (Porta et al 2008, 2009). The E index represents the skewness of the decrement or increment distribution between two consecutive heartbeats. Several authors conclude that a cardiac sympathoexcitation associated with a vagal withdrawal induced by physical stressors (head-up tilt test, active orthostasis) causes the time irreversibility indices (P%, G%, E) to rise (Porta et al 2006, 2007, Chladekova et al 2012. Unexpectedly, our results revealed significant reverse behaviour of these indices: G% and E decreased in response to both acute cognitive stressors, suggesting that heart rate accelerations are more rapid than decelerations. Additionally, only the mental arithmetic test evoked adecline in Porta’s index. In the following paragraphs, several possible explanations for this controversial finding will be proposed. First, the heart rate asymmetry is a new and thus far poorly understood phenomenon that could be potentially caused by various physiological mechanisms. RSA—the coupling of heart rate oscillations to the respiratory cycle—as a principal physiological mechanism of the short-term HRV is influenced by an asymmetric pattern of a single breathing cycle, i.e. inspiration and expiration times are not equal and the expiratory phase lasts longer in a healthy human (Piskorski and Guzik 2011). We hypothesize that the reduced RSA magnitude discovered in this work could contribute to the lower heart rate time asymmetry in response to mental stress in healthy students. In other words, the asymmetric breathing pattern is not so effectively transferred into heart rate oscillations. Moreover, Porges’ polyvagal theory (1995, 2009) hypothesized that RSA could reflect the cardiovagal as well as the emotional regulation, indicating overall autonomic flexibility, and another study referred to the brain s emotional apparatus as a key mechanism influencing stress reactivity (Lovallo 2011). From this point of view, the vagal withdrawal reflected in reduced RSA amplitude associated with different emotional dispositions might represent potential mechanisms responsible for heart rate asymmetry changes observed in response to stress. Further, the vagal impact on the heart rate asymmetry has been established by pharmacological autonomic blockade using 1329

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atropine and scopolamine (Karmakar et al 2012). However, correlation analysis did not reveal a significant association between linear measures, including HF-HRV, as a marker of RSA (cardiovagal index), and time irreversibility indices. This could be explained by the underlying mathematical representation of the heart rate asymmetry, which is based on the irreversible dynamics of the heart rate increments and decrements from one beat to another—asymmetry originating from the different structures of decelerations and accelerations. This asymmetry could also be affected by baroreflex characteristics (Piskorski and Guzik 2011). If baroreflex influences heart rate asymmetry, our results could indicate stress-related baroreflex changes in response to mental stress. Nevertheless, this hypothesis remains speculative and further research is therefore needed to clarify this hypothesis. Second, stress-related changes in heart time irreversibility could depend on the underlying response system evoked by different stressors, i.e. active versus passive (Treiber et al 2003). While active cognitive stressors are associated predominantly with cardiac beta-adrenergic activity, passive stressors (e.g. passive/active orthostasis) elicit physiological responses reflecting alpha-adrenergic activation (Schneider et al 2003). Furthermore, psychological stressors are complex events that challenge the cardiac reactivity in multiple brain regions including cortical (prefrontal cortex), subcortical (hypothalamus, brainstem) and peripheraladrenoreceptor function (Lovallo 2011). In cognitive stressors, direct pathways from prefrontal regions are able to activate hypothalamic and brainstem autonomic control centres, leading to changes in the cardiac control system balance (Lovallo and Gerin 2003). In contrast, neurocardiac reactivity induced by an orthostatic test is associated with autonomic regulatory subcortical centres (hypothalamus, brainstem), and at the level of the peripheral organ the heart. Therefore, diverse neurophysiological regulatory pathways could explain the controversial findings in heart rate time irreversibility indices between active and passive stressors. We suggest that our results could contribute to better characterize the irreversibility index behaviour indicating a potential novel response pattern to cognitive stressors. The Porta index compared the numbers of increments and decrements (i.e. RR decelerations and accelerations). In contrast to P%, the Guzik index also considers the magnitude of the difference between two heartbeats. The information about the skewness of the increment/decrement distribution between two heartbeats is represented by the Ehlers index. While the G% and E indices were sensitive to detect a shift of sympathovagal balance in both stressors, the P% index showed significant changes only with the arithmetic test, suggesting that heart rate decelerations and accelerations are related to actual sympathetic/parasympathetic activity in response to mental stress. Importantly, the correlation analysis revealed a close relationship between the G% and E parameters, but no significant mutual relations were found between P% and the given indices (G%, E). Thus, these different indices may provide partially independent information and their simultaneous quantification might useful to detect the irreversibility comprehensively. Importantly, correlation analysis revealed no significant relations between symbolic dynamics and time irreversibility parameters. Thus, we assume that both methods of HRV nonlinear analysis could provide independent information related to qualitative features in complex neurocardiac control underlying heart rate nonlinear oscillations. 4.1. Study limitation

In this study, the breathing pattern was not controlled. Our studied group breathed spontaneously to exclude a degree of mental effort evoked by controlled breathing, which may affect the HRV indices. However, the assessment of the interaction between respiratory and HRV parameters could have given important information. Further research in this field with continuous respiratory parameter recording and subsequent analysis is needed. 1330

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5. Conclusion Our study revealed that qualitative features in complex dynamics of heart rate modulation are altered during mental acute stress. Furthermore, symbolic dynamics indices 0V% and 2LV% could reflect a shift in sympathovagal balance in response to stress, and time asymmetry was reduced in mental stress. Therefore, HRV nonlinear analysis based on symbolic dynamics and time irreversibility could provide additional important and mutually independent information related to complex neurocardiac integrity in acute/chronic stress. It could help to elucidate the pathway linking health and stress-related disease. Acknowledgments This study was supported by the European Centre of Excellence for Perinatological Research no. 26220120016, VEGA no. 1/0087/14, grant UK/299/2013. References Abukonna A, Yu X, Zhang C and Zhang J 2013 Volitional control of the heart rate Int. J. Psychophysiol. 90 143–8 Bailon R, Sornmo L and Laguna P 2006 A robust method for ECG-based estimation of the respiratory frequency during stress testing IEEE Trans. Biomed. Eng. 53 1273–85 B¨ar K J, Boettger M K, Koschke M, Schulz S, Chokka P, Yeragani V K and Voss A 2007 Non-linear complexity measures of heart rate variability in acute schizophrenia Clin. Neurophysiol. 118 2009–15 Baumert M, Lambert G W, Dawood T, Lambert E A, Esler M D, McGrane M, Barton D, Sanders P and Nalivaiko E 2009 Shortterm heart rate variability and cardiac norepinephrine spillover in patients with depression and panic disorder Am. J. Physiol. Heart Circ. Physiol. 297 H674–9 Berntson G G et al 1997 Heart rate variability: origins, methods, and interpretive caveats Psychophysiology 34 623–48 Bettermann H, Kroz M, Girke M and Heckamnn C 2001 Heart rate dynamics and cardiorespiratory coordination in diabetic and breast cancer patients Clin. Physiol. 21 411–20 Bornas X, Llabres J, Noguera M, Lopez A M, Gelabert J M and Vila I 2006 Fear induced complexity loss in the electrocardiogram of flight phobics: a multiscale entropy analysis Biol. Psychol. 73 272–9 Burger A J, D’Elia J A, Weinrauch L A, Lerman I and Gaur A 2002 Marked abnormalities in heart rate variability are associated with progressive deterioration of renal function in type I diabetic patients with overt nephropathy Int. J. Cardiol. 86 281–7 Casali K R, Casali A G, Montano N, Irigoyen M C, Macagnan F, Guzetti S and Porta A 2008 Multiple testing strategy for the detection of temporal irreversibility in stationary time series Phys. Rev. E 77 066204 Chida Y and Steptoe A 2010 Stress reactivity and its association with increased cardiovascular risk: a role for the sympathetic nervous system? Hypertension 55 e21 Chladekova L, Czippelova B, Turianikova Z, Tonhajzerova I, Calkovska A, Baumert M and Javorka M 2012 Multiscale time irreversibility of heart rate and blood pressure variability during orthostasis Physiol. Meas. 33 1747–56 Cover T M and Thomas J A 1991 Elements of Information Theory (New York: Wiley) Crowley O V, McKinley P S, Burg M M, Schwartz J E, Ryff C D, Weinstein M, Seeman T E and Sloan R P 2011 The interactive effect of change in perceived stress and trait anxiety on vagal recovery from cognitive challenge Int. J. Psychophysiol. 82 225–32 De La Cruz Torres B and Naranjo Orellana J 2010 Multiscale time irreversibility of heartbeat at rest and during aerobic exercise Cardiovasc. Eng. 10 1–4 Dishon-Berkovit M and Algom D 2000 The Stroop effect: it is not the robust phenomenon that you have thought it to be Mem. Cogn. 28 1437–49 Ehlers C L, Havstad J, Prichard D and Theiler J 1998 Low doses of ethanol reduce evidence for nonlinear structure in brain activity J. Neurosci. 18 7474–86 1331

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