Simulating Dynamics of Circulation in the Awake State and Different Stages of Sleep Using Non-autonomous Mathematical Model With Time Delay

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

The intensity of oscillations of the photoplethysmographic waveform variability at frequencies 0.04–0.4 Hz is effective marker of hypertension and coronary artery disease in males

Background: It is believed that the intensity of oscillations in the photoplethysmographic waveform variability reflects the activity of vascular regulatory mechanisms. However, the relationship of such fluctuations with the state of health is poorly understood. Purpose: The aim of our study was to assess the possibility of using spectral indices that reflect the intensity of oscillations of the photoplethysmographic waveform variability at frequencies 0.04-0.4 Hz as markers of hypertension and coronary artery disease. We did not study women to exclude the influence of menopause and sex hormones on the results. Materials and Methods: We compared synchronous 10-minute records of finger photoplethysmogram and respiration at rest in 30 healthy males (48.8 ± 4.5 years; data presented as Mean ± SD) versus 30 patients with hypertension (aged 49.0 ± 4.3 years) versus 30 patients with stable coronary artery disease (49.2 ± 4.8 years). Percentages of high-frequency and low-frequency ranges in the total power of photoplethysmographic waveform variability spectrum (HF% and LF%), and LF/HF ratio were assessed. Results: HF% are subject to by 2- to 5-fold increase in hypertensive patients (p < .001) and up to an 8-fold increase in patients with coronary artery disease (p < .001) when compared with healthy persons. On the contrary, LF% is reduced by 1.5-5 times in all patients when compared with healthy people (p < .001). We identified cut-off points for each photoplethysmographic index to distinguish patients with coronary artery disease or hypertension from healthy subjects. Multiple logistic regression models based on photoplethysmographic waveform variability indices had sufficient sensitivity and specificity for patients with hypertension or coronary artery disease. Conclusion: Frequency-domain indices of photoplethysmographic waveform variability (in particular, HF%, LF%, and LF/HF) are sufficiently sensitive and specific markers of hypertension and coronary artery disease in adult males

Problem of power spectra estimation in application to the analysis of heart rate variability

We investigated how the parameters of the spectral analysis affect standard deviation and error of the estimation of well-known indices for the heart rate variability. We compared the nonparametric Fourier transform to the parametric approach based on autoregressive models. We also investigated how the precision of the indices estimation depends on the choice of the window function, parameterization of the Bartlett’s method, and the lengths of time series. For each set of parameters, we calculated the sensitivity and specificity of the resulting indices when diagnosing arterial hypertension. To isolate and investigate the errors caused by inaccuracy of the spectral analysis itself, we conducted our study using the mathematical models of heart rate variability for healthy subjects and arterial hypertension patients, for which the correct values of the spectral indices are known. The obtained results suggest that the analysis of 20-min signals, comparing to 5-min signals, significantly decreases the standard deviation of the estimations and increases both their sensitivity and specificity. We found no advantages of using the parametric approach over the Fourier transform. We have shown that application of the Hann’s window function and normalization of the spectral indices decreases the sensitivity and specificity of the medical diagnostics

Dynamics of 0.1 Hz Oscillations Synchronization in Cardiovascular System during the Treatment of Acute Myocardial Infarction Patients

Aim: The aim was the studying of synchronization between 0.1 Hz oscillations in heart rate (HR) and plethysmographic peripheral microcirculation (PM) in acute myocardial infarction (AMI) patients and in healthy subjects. Material and Method: 12 healthy volunteers aged 26±5 years and 125 patients with AMI aged 65±9 years were involved in the study. Simultaneous registration of electrocardiogram and photoplethysmogram were performed during 10 min. In AMI patients the signals were recorded twice: the first record was done during 3-5 days after AMI, the second record was done during the third week after AMI. Phase differences between HR and PM oscillations were used to measure the degree of synchronization (S). Data are submitted as medians with inter-quartile ranges (25%, 75%). Results: S was 65.8% (50.5%; 79.5%) in healthy subjects whereas in AMI patients at the first week after AMI S was 16.3% (9.4%; 24.6%) (p<0.001). In records made at the third week after AMI index S was 18.4% (11.2%; 28.2%). Two groups of AMI patients were identified on the basis of individual S dynamics. In 100 AMI patients no dynamics of S was observed during the observation period and in 25 AMI patients the increase of S was observed. The group of AMI patients with increase of S had greater HR values during the first week after AMI. Conclusion: The index S of synchronization of 0.1 Hz oscillations in HR and PM appears to be a sensitive indicator of autonomic control dynamic disturbances in AMI patients

Comparing the spectral properties of the laser-induced acoustic responses from blood and cancer cells in vitro

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

Biomarkers of the psychophysiological state during the cognitive tasks estimated from the signals of the brain, cardiovascular and respiratory systems

Diagnostics of the psychophysiological state at rest and under stressful conditions is an important problem. We tested various biomarkers of the psychophysiological state of healthy volunteers at rest and while completing stress-inducing cognitive tasks, namely the Stroop color word test and mental arithmetic test. We tested the biomarkers based on the analysis of electroencephalograms, respiratory signals, and the signals of cardiovascular system. We investigated both the individual characteristics of these signals in the low-frequency range (less than 0.5 Hz), and characteristics of their interaction. According to our results, the most sensitive biomarkers of cognitive task stress are nonlinear phase coherence between the 0.15 and 0.40 Hz oscillations in the respiratory signal and heart rate variability, and integral power of the 0.15–0.40 Hz oscillations in the frontal lobe EEG leads