Search

A change in the human psychophysiological state, caused by stress in particular, affects the processes of autonomic control, the activity of which is reflected in infra-slow oscillations of brain potentials with a frequency of less than 0.5 Hz. We studied the infra-slow oscillations in scalp electroencephalogram (EEG) signals in the frequency ranges of 0.05–0.15 Hz and 0.15–0.50 Hz that are associated with the processes of sympathetic and parasympathetic control, respectively, in healthy subjects at rest and during stress-inducing cognitive tasks. The power spectra of EEG signals, the phase coherence coefficients, and indices of directional coupling between the infra-slow oscillations in the signals of different EEG leads were analyzed. We revealed that, compared with the state of rest, the stress state is characterized by a significant decrease in the power of infra-slow oscillations and changes in the structure of couplings between infra-slow oscillations in EEG leads. In particular, under stressful conditions, a decrease in both intrahemispheric and interhemispheric coupling between EEG leads occurred in the range of 0.05–0.15 Hz, while a decrease in intrahemispheric and an increase in interhemispheric couplings was observed in the range of 0.15–0.50 Hz.

We propose a mathematical model of the human cardiovascular system. The model allows one to simulate the main heart rate, its variability under the influence of the autonomic nervous system, breathing process, and oscillations of blood pressure. For the first time, the model takes into account the activity of the cerebral cortex structures that modulate the autonomic control loops of blood circulation in the awake state and in various stages of sleep. The adequacy of the model is demonstrated by comparing its time series with experimental records of healthy subjects in the SIESTA database. The proposed model can become a useful tool for studying the characteristics of the cardiovascular system dynamics during sleep.

Irregularity is an important aspect of the cardiovascular system dynamics. Numerical indices of irregularity, such as the largest Lyapunov exponent and the correlation dimension estimated from interbeat interval time series, are early markers of cardiovascular diseases. However, there is no consensus on the origin of irregularity in the cardiovascular system. A common hypothesis suggests the importance of nonlinear bidirectional coupling between the cardiovascular system and the respiratory system for irregularity. Experimental investigations of this theory are severely limited by the capabilities of modern medical equipment and the nonstationarity of real biological systems. Therefore, we studied this problem using a mathematical model of the coupled cardiovascular system and respiratory system. We estimated and compared the numerical indices of complexity for a model simulating the cardiovascular dynamics in healthy subjects and a model with blocked regulation of the respiratory frequency and amplitude, which disturbs the coupling between the studied systems.

Background ― The treatment of the cancer, especially in more aggressive metastatic forms is more effective at early disease stage. However, existing diagnostic techniques are not sensitive enough for early cancer detection. An alternative, perspective diagnostic approach can be based on photoacoustic (PA) method of irradiation of cancer cells in biotissue, blood and lymph by laser pulses. The fast thermal expansion of heated zones into cells associated with intrinsic or artificial PA contrast agents leads to generation of acoustic waves detected with ultrasound transducers. In particular, melanoma cells with melanin as a PA marker are darker than normal red blood cells and, therefore, produce greater acoustic responses. This technique can theoretically detect even a single cancer cell in the tissue and blood background; however, a robust algorithm of automated response detection is yet to be developed. Objective ― The main aim is to develop the approach for data pre-analysis that can improve the sensitivity and noise resistance of the automated in individual cancer cell detection algorithm, based on estimation of the amplitude of the acoustic responses. Methods ― Acoustic responses were obtained from a round polyurethane tube with human blood, or solution of the mouse melanoma cells in 10 mol/L concentration. In control experiments the laser was blocked by an opaque film. Many (up to 1000) acoustic responses were obtained from normal blood cells and pigmented cancer cells. Spectral analysis of the acoustic responses was used to find the spectral ranges that provide valuable diagnostic information with the sufficient signal-to-noise ratio. Results ― It was estimated that relevant diagnostics information in the acoustic responses is limited to the 0-12 MHz frequency band. Application of the 8th order low-pass Butterwort filter with 12 MHz cut-off frequency improved the signal-to-noise ratio from 21.14±10.39 to 110.81±56.94 for the cancer-related responses, and from 1.04±0.1 to 2.23±0.33 for the normal blood responses. Conclusions ― Adoption of low-pass filtering during the pre-analysis of acoustic responses results in better sensitivity of automated cancer cells detection algorithm.

Aim of the study is to apply the analysis of phase dynamics to investigation of the coupling patterns between heart rate variability, respiration and peripheral circulation in healthy subjects at rest and after moderate physical exercises. Material and Methods ― 30-minutes electrocardiogram (ECG), photoplethysmogram (PPG) and respiration records were obtained from healthy subjects aged 22±2 (mean ± standard deviation) before and after active Martine Kushelevsky test (20 squats in 30 seconds). The coherence function was estimated between all the signals from each subject, and the phase dynamics modeling was used to detect the directional coupling in high-frequency (HF; 0.14-0.40 Hz) and low-frequency ranges (LF; 0.04-0.14 Hz). Results ― At rest (before the physical activity) no statistically significant couplings were detected between the HF rhythms of respiration and heart rate (RR intervals). For the HF rhythms in respiration and PPG, the influence respirationPPG was detected. No couplings were detected between the LF rhythms. After the moderate physical exercise, predominant direction of the influence was RR intervals  PPG in the LF range and both RR intervals  respiration and PPG  respiration in the HF range. The influence PPG  respiration was delayed by several seconds. Conclusion ― adaptation to the moderate physical activity has led to the decrease in the overall coherence and changes in patterns of directional coupling between the LF and HF rhythms in respiration, heart rate and peripheral circulation. The obtained results confirm nonlinearity and complexity of the coupling patterns in cardiovascular system.

The aim of study is to investigate effectiveness of cross-recurrence analysis for the detection of coupling between the loops of heart rate and vessel tone sympathetic control. The cross-recurrence analysis is applied to the interbeat intervals and photopletysmographic signals from the mathematical model of cardiovascular system and from adults and newborn children. The model is examined under the conditions of the four experiments: with fully operational autonomic control, autonomic control weakened by 40%, autonomic control weakened by 70% and autonomic blockade. The coupling is most pronounced in adult subjects and the model with fully operational autonomic control, while it is absent under the autonomic blockade. Autonomic control is also not fully developed in newborn children. As the result, smaller coupling strength is expected. The results of cross-recurrence analysis of the model and experimental data are compared with the results of coupling detection based on the analysos of phase synchronization. Synchronization index demonstrates good correlation with the coupling strength in the model and weak coupling in newborn children in relation to adult subjects. In both cases, no correlation is observed between the synchronization index and the results of cross-recurrence analysis.

Objective ― This study compares the statistical characteristics of interaction between 0.1 Hz oscillations in heart rate variability (HRV) and photoplethysmographic waveform variability (PPGV) in healthy subjects and patients with myocardial infarction (MI). Material and Methods ― We studied 23 healthy subjects (20 men and 3 women, aged 26±3 years) and 23 patients (12 men and 11 women, aged 52±4 years) at about one month after MI. The 10-minute signals of simultaneously recorded electrocardiogram (ECG) and photoplethysmogram (PPG) were studied. We calculated the total percentage of phase synchronization between the studied 0.1 Hz oscillations and estimated the distribution functions of duration of synchronous and non-synchronous epochs, the variability of basic frequency of oscillations, and variance of phase noises in 0.1 Hz oscillations in HRV and PPGV. Results ― The total percentage of phase synchronization between 0.1 Hz oscillations is significantly greater in healthy subjects than in MI patients (47±3% and 26±4%, respectively). Significant difference between these two groups in the distribution of duration of synchronous and non-synchronous epochs was not revealed. The MI patients had greater variance between the basic frequencies of 0.1 Hz oscillations in HRV and PPGV than healthy subjects. This phenomenon correlates with the increased level of phase noises in the records of MI patients. Conclusion ― The quality of synchronization between 0.1 Hz oscillations in HRV and PPG is associated with the strength of influence of external factors (noises) and variability of the basic frequency of these oscillations.

This paper describes in detail a new method proposed by authors for quantitative estimation of the strength of synchronization between the low-frequency oscillations (with the main frequency of about 0.1 Hz) in the heart rate variability (HRV) and photoplethysmogram (PPG). Calculation of index value is followed by statistical significance control. The proposed method is applied for the analysis of 1056 pairs of HRV and PPG signals obtained from patients having different clinical status. Methodological recommendations are developed for method application in clinical studies.