The dynamics of cardiac signals can be studied using methods for nonlinear analysis of heart rate variability (HRV). The methods that are used in the article to investigate the fractal, multifractal and informational characteristics of the intervals between heartbeats (RR time intervals) are: Rescaled Range, Detrended Fluctuation Analysis, Multifractal Detrended Fluctuation Analysis, Poincaré plot, Approximate Entropy and Sample Entropy. Two groups of people were studied: 25 healthy subjects (15 men, 10 women, mean age: 56.3 years) and 25 patients with arrhythmia (13 men, 12 women, mean age: 58.7 years). The results of the application of the methods for nonlinear analysis of HRV in the two groups of people studied are shown as mean ± std. The effectiveness of the methods was evaluated by t-test and the parameter Area Under the Curve (AUC) from the Receiver Operator Curve (ROC) characteristics. The studied 11 parameters have statistical significance (p < 0.05); therefore, they can be used to distinguish between healthy and unhealthy subjects. It was established by applying the ROC analysis that the parameters H_q=2(MFDFA), F(α)(MFDFA) and SD₂(Poincaré plot) have a good diagnostic value; H(R/S), α₁(DFA), SD₁/SD₂(Poincaré plot), ApEn and SampEn have a very good score; α₂(DFA), α_all(DFA) and SD₁(Poincaré plot) have an excellent diagnostic score. In conclusion, the methods used for nonlinear analysis of HRV have been evaluated as effective, and with their help, new perspectives are opened in the diagnosis of cardiovascular diseases.

This article presents a new 3D extreme game for virtual reality (VR), which is used to evaluate the impact of generated mental stress on the cardiological state of the playing individuals. The game was developed using Java 3D and Blender. Generated stress is investigated by recording electrocardiograms for 20 min and determining heart rate variability (HRV) parameters in the time and frequency domains and by non-linear visual and quantitative analysis methods, such as the Rescaled Range (R/S) method, Poincarè plot, Recurrence plot, Approximate (ApEn), and Sample Entropy (SampEn). The data of 19 volunteers were analyzed before and immediately after the game, and a comparative analysis was made of two types of VR: immersive and non-immersive. The results show that the application of immersive VR generates higher mental stress levels than non-immersive VR, but in both cases, HRV changes (decreases), but more significantly in immersive VR. The results of this research can provide useful information about the functioning of the autonomic nervous system, which regulates the reactions of the human body during mental stress, to help in the early detection of potential health problems.

This article analyses methods and approaches for assessing heart rate variability (HRV). One of the approaches is to study the impact of sports, in particular running athletics on HRV. As a result of the research, it was found that sports increase HRV by increasing the sympathetic share. The HRV method is influenced by various factors: diseases, external factors, physiological and lifestyle factors. Those that influence the athletic achievements of athletes are also considered. The impact of stress on HRV can be studied by creating 3D serious games to simulate various stressful situations and to study their impact on HRV. The article presents virtual reality systems, which are of two types: with immersion and without immersion and their application in the study of HRV. It follows from the analysis that the level of sympathetic tone in the analysis of HRV using virtual reality depends on the degree of immersion. Another approach to assessing HRV is to monitor patients at night to determine parasympathetic tone of the autonomic nervous system. For the accurate analysis of HRV, the mathematical methods used are important, which are mainly divided into linear and nonlinear. Spectral analysis is an example of linear, and Poincarè plot is nonlinear.

Background: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21 ± 2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical comparisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, paired t-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimates η²). Results: Immediately after training, significant decreases in SDNN (↓ 35%), RMSSD (↓ 40%), and pNN50 (↓ 55%), accompanied by increases in LF/HF (↑ 32%), were observed. DFA α1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitudinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications.

In the article, a comparative analysis is performed regarding the accuracy parameter in determining the degree of self-similarity of fractal processes between the following methods: Variance-Time plot, Rescaled Range (R/S), Wavelet-based, Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). To evaluate the methods, fractal processes based of Fractional Gaussian Noise were simulated and the dependence between the length of the simulated process and the degree of self-similarity was investigated by calculating the Hurst exponent (H > 0.5). It was found that the Wavelet-based, DFA and MFDFA methods, with a process length greater than 2¹⁴ points, have a relative error of the Hurst exponent is less than 1%. A methodology for the Wavelet-based method related to determining the size of the scale and the wavelet algorithm was proposed, and it was investigated in terms of the exact determination of the Hurst exponent of two algorithms: Haar and Daubechies with different number of coefficients and different values of the scale. Based on the analysis, it was determined that the Daubechies algorithm with 10 coefficients and scale (i = 2, j = 10) has a relative error of less than 0.5%. The three most accurate methods are applied to the study of real cardiac signals of two groups of people: healthy and unhealthy (arrhythmia) subjects. The results of the statistical analysis, using the t-test, show that the proposed methods can distinguish the two studied groups and can be used for diagnostic purposes.

This study examines the effects of different stereoscopic technologies on physiological responses in immersive virtual reality (VR) environments. Five participant groups were evaluated: a control group (no stereoscopy) and four groups using anaglyph, passive, active glasses, or VR helmets. Heart rate variability (HRV) was measured in both time (MeanRR, SDNN, RMSSD, pNN50) and frequency (LF, HF, LF/HF) domains to assess autonomic nervous system activity. Active, polarized glasses and VR helmets significantly reduced SDNN and RMSSD compared to the control group (p < 0.01), with VR helmets causing the largest decrease (MeanRR −70%, RMSSD −51%). Anaglyph glasses showed milder effects. Nonlinear analysis revealed reduced entropies and Hurst parameter in highly immersive conditions, indicating impaired fractal heart rate structure and increased physiological load. These results demonstrate a clear relationship between immersion level and cardiovascular response, emphasising that higher immersion increases physiological stress. The scientific contribution lies in the combined application of linear and nonlinear HRV analysis to systematically compare different stereoscopic display types under controlled gaming immersion. The study proposes a practical methodology for assessing HRV in VR settings, which can inform the ergonomic design of VR systems and ensure users’ physiological safety. By highlighting the differential impacts of stereoscopic technologies on HRV, the findings offer guidance for optimising VR visualisation to balance immersive experience with user comfort and health.