Systolic Blood Pressure Time Series Research Articles

Traumatic Systolic Blood Pressure Modeling: A Spectral Gaussian Process Regression Approach with Robust Sample Covariates

Physiological vital signs acquired during traumatic events are informative on the dynamics of the trauma and their relationship with other features such as sample-specific covariates. Non-time dependent covariates may introduce extra challenges in the Gaussian Process (<img src=image/13425527_01.gif>) regression, as their main predictors are functions of time. In this regard, the paper introduces the use of Orthogonalized Gnanadesikan-Kettering covariates for handling such predictors within the Gaussian process regression framework. Spectral Bayesian <img src=image/13425527_01.gif> regression is usually based on symmetric spectral frequencies and this may be too restrictive in some applications, especially physiological vital signs modeling. This paper builds on a fast non-standard variational Bayes method using a modified Van der Waerden sparse spectral approximation that allows uncertainty in covariance function hyperparameters to be handled in a standard way. This allows easy extension of Bayesian methods to complex models where non-time dependent predictors are available and the relationship between the smoothness of trend and covariates is of interest. The utility of the methods is illustrated using both simulations and real traumatic systolic blood pressure time series data.

Predicting Systolic Blood Pressure in Real-Time Using Streaming Data and Deep Learning

High systolic blood pressure causes many problems, including stroke, brain attack, and others. Therefore, examining blood pressure and discovering issues related to it at the right time can help prevent the occurrence of health problems. Nowadays, health-based data brings a new dimension to healthcare by exploiting the real-time patients’ data to early detect systolic blood pressure (SBP). Furthermore, technologies typically associated with smart and real-time data processing add value in the healthcaredomain, including artificial intelligence, data analytic technologies, and stream processing technologies. Thus, this paper introduces a systolic blood pressure prediction system that can predict SBP in real-time and, therefore, can avoid health problems that may stem from sudden high blood pressure. The proposed system works through two components, namely, developing an offline model and an online prediction pipeline. The aim of developing an offline model module is to develop the model using investigate different deep learning models to achieve the smallest root mean square error. It has been developed using Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Short-Term Memory (BI-LSTM), Gated Recurrent Units (GRU) models andMedical Information Mart for Intensive Care (MIMC II) SBP time-series dataset. The online prediction pipeline module is using Apache Kafka and Apache Spark to predict the near future of SBP in real-time using the best deep learning model and SBP streaming time-series data. The experimental results indicate that the BI-LSTM model has achieved the best performance using three hidden layers, and it is used to predict the near future of SBP in real-time.

Whole body exercise training improves blood pressure and postural balance responses during orthostatic stress onset in older women

The purpose of the current study was to investigate the effects of whole body exercise training on spontaneous baroreflex sensitivity at rest, blood pressure and postural balance responses during orthostatic onset in older women. The sample composed by eight healthy older women (65±3 yrs.). R‐R intervals and blood pressure were continuously measured from a standard electrocardiogram (ECG) and infrared finger plethysmography, respectively, at rest (10min) and during orthostatic position (7min). Spontaneous baroreflex sensitivity at rest was obtained by spectral method in the low‐frequency component of R‐R and systolic blood pressure time series (αLF). The nadir point was considered the lower value of mean arterial pressure (MAP) during transition from supine to orthostatic position. The percentage changes (%ΔMAP) from supine to orthostatic nadir were calculated. The time of MAP oscillations during orthostatic onset (MAPtime) was calculated by the sum of time to nadir and time to recovery (nearest value of MAP at rest). In addition, during the ORT onset (15s) the center of pressure displacements (COP) were acquired by a force plate, and COP distance and speed were calculated. The whole body exercise training protocol (rowing ergometer) consisted of three weekly sessions (30 minutes) at 60–80% of maximal heart rate (defined by a previous maximal exercise test) for 10 weeks. The volunteers were evaluated pre and post‐trainning. Student‐t tests for paired samples was employed (p≤0.05) to compare all variables. At rest, αLF (pre= 3.9 ±1.7 vs. post= 5.3 ±1.1 ms2 p&lt;0.001) increased post‐training and MAP (pre= 85.1 ±10.3 vs. post= 77.4 ±7.8 mmHg p&lt; 0.02) decreased. No differences in CO (pre= 4.526 ±0.7697 vs. post= 5.058 ±2.104 L/min p=0.02) and SV (pre= 66.33 ±19.94 vs. post= 72.18 ±29.67 mL p=0.01) were found at rest. Although %ΔMAP (pre= −0.3 ±0.2 vs. post= −0.3 ±0.16 mmHg p&lt; 0.19) was not different pre and post training, MAPtime (pre= 14.16 ±3.996 vs. post= 10.94 ±5.637 seconds p&lt;0.04) decreased post‐training protocol. Moreover. COP distance (pre= 673.7 ±74.9 vs. post= 560.3 ±95.1 mm p&lt; 0.03) and speed (pre= 44.9 ±4.9 vs. post= 37.3 ±6.3 mm/s p&lt;0.03) were lower post‐training. Whole body exercise training improves spontaneous baroreflex sensitivity at rest mean arterial pressure and postural balance adjustments during orthostatic stress onset in older women. Research ethics committee (CEP‐851.371/14).Support or Funding InformationFoundation for Research Support of the State of Rio de Janeiro ‐ FAPERJ (E‐6/110.079/2013), Coordination for the Improvement of Higher Education Personnel ‐ CAPES.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Analysis of diagonals in cross recurrence plots between heart rate and systolic blood pressure during supine position and active standing in healthy adults.

The inter beat interval (IBI) duration and systolic blood pressure (SBP) are cardiovascular variables related through several feedback mechanisms. We propose the analysis of diagonal lines in cross recurrence plots (CRPs) from IBI and SBP embedded within the same phase space to identify events where trajectories of both variables concur. The aim of the study was to describe the relationship between IBI and SBP of healthy subjects using CRP and diagonal analysis during baseline condition-supine position (SP)-and how the relationship changes during the physiological stress of active standing (AS). IBI and SBP time series were obtained from continuous blood pressure recordings during SP and AS (15 min each) in 19 young healthy subjects. IBI and SBP time series were embedded within a five-dimensional phase space using an embedding delay estimated from cross correlation between IBI and SBP. During SP, mean CRP showed high determinism (≥85%) and also brief but repeated events where both variables stay within a reduced space. Most quantitative recurrences analysis indexes of CRP increased significantly (p < 0.05) during AS. CRP analysis showed short diagonals indicating a very strong deterministic relationship between IBI and SBP with intermittent unlocking periods. The strength of IBI and SBP relationship increased during the physiological stress of AS. The CRP method allowed a rigorous quantitative description of the deterministic association between these two variables. Diagonal lines were intermittent and not always parallel, showing that there is not a defined and unique rhythm. This suggests the activation of different influences at different times and with different precedence between the heart rate and blood pressure in response to AS.

Heart Rate and Systolic Blood Pressure Variability in the Time Domain in Patients with Recent and Long-Standing Diabetes Mellitus.

Diabetes Mellitus (DM) affects the cardiovascular response of patients. To study this effect, interbeat intervals (IBI) and beat-to-beat systolic blood pressure (SBP) variability of patients during supine, standing and controlled breathing tests were analyzed in the time domain. Simultaneous noninvasive measurements of IBI and SBP for 30 recently diagnosed and 15 long-standing DM patients were compared with the results for 30 rigorously screened healthy subjects (control). A statistically significant distinction between control and diabetic subjects was provided by the standard deviation and the higher moments of the distributions (skewness, and kurtosis) with respect to the median. To compare IBI and SBP for different populations, we define a parameter, α, that combines the variability of the heart rate and the blood pressure, as the ratio of the radius of the moments for IBI and the same radius for SBP. As diabetes evolves, α decreases, standard deviation of the IBI detrended signal diminishes (heart rate signal becomes more “rigid”), skewness with respect to the median approaches zero (signal fluctuations gain symmetry), and kurtosis increases (fluctuations concentrate around the median). Diabetes produces not only a rigid heart rate, but also increases symmetry and has leptokurtic distributions. SBP time series exhibit the most variable behavior for recently diagnosed DM with platykurtic distributions. Under controlled breathing, SBP has symmetric distributions for DM patients, while control subjects have non-zero skewness. This may be due to a progressive decrease of parasympathetic and sympathetic activity to the heart and blood vessels as diabetes evolves.

Cardiovascular regulation during sleep quantified by symbolic coupling traces

Sleep is a complex regulated process with short periods of wakefulness and different sleep stages. These sleep stages modulate autonomous functions such as blood pressure and heart rate. The method of symbolic coupling traces (SCT) is used to analyze and quantify time-delayed coupling of these measurements during different sleep stages. The symbolic coupling traces, defined as the symmetric and diametric traces of the bivariate word distribution matrix, allow the quantification of time-delayed coupling. In this paper, the method is applied to heart rate and systolic blood pressure time series during different sleep stages for healthy controls as well as for normotensive and hypertensive patients with sleep apneas. Using the SCT, significant different cardiovascular mechanisms not only between the deep sleep and the other sleep stages but also between healthy subjects and patients can be revealed. The SCT method is applied to model systems, compared with established methods, such as cross correlation, mutual information, and cross recurrence analysis and demonstrates its advantages especially for nonstationary physiological data. As a result, SCT proves to be more specific in detecting delays of directional interactions than standard coupling analysis methods and yields additional information which cannot be measured by standard parameters of heart rate and blood pressure variability. The proposed method may help to indicate the pathological changes in cardiovascular regulation and also the effects of continuous positive airway pressure therapy on the cardiovascular system.

Breathing cardiovascular variability and baroreflex in mechanically ventilated patients

Heart rate and blood pressure variations during spontaneous ventilation are related to the negative airway pressure during inspiration. Inspiratory airway pressure is positive during mechanical ventilation, suggesting that reversal of the normal baroreflex-mediated pattern of variability may occur. We investigated heart rate and blood pressure variability and baroreflex sensitivity in 17 mechanically ventilated patients. ECG (RR intervals), invasive systolic blood pressure (SBP), and respiratory flow signals were recorded. High-frequency (HF) amplitude of RR and SBP time series and HF phase differences between RR, SBP, and ventilatory signals were continuously computed by Complex DeModulation (CDM). Cross-spectral analysis was used to assess the coherence and the gain functions between RR and SBP, yielding baroreflex sensitivity indices. The HF phase difference between SBP and ventilatory signals was nearly constant in all patients with inversion of SBP variability during the ventilator cycle compared with cycling with negative inspiratory pressure to replicate spontaneous breathing. In 12 patients (group 1), the phase difference between RR and ventilatory signals changed over time and the HF-RR amplitude varied. In the remaining five patients (group 2), RR-ventilatory signal phase and HF-RR amplitude showed little change; however, only one of these patients exhibited a RR-ventilatory signal phase difference mimicking the normal pattern of respiratory sinus arrhythmia. Spectral coherence between RR and SBP was lower in the group with phase difference changes. Positive pressure ventilation exerts mainly a mechanical effect on SBP, whereas its influence on HR variability seems more complex, suggesting a role for neural influences.

Autonomic regulation during orthostatic stress in highlanders: comparison with sea‐level residents

This report is a comparison of orthostatic tolerance and autonomic function in three groups of high-altitude dwellers: Andeans with and without chronic mountain sickness (CMS) and healthy Ethiopians. Results are compared with those from healthy sea-level residents. The aim was to determine whether different high-altitude populations adapted differently to the prevailing hypobaric hypoxia. Orthostatic tolerance was assessed using a test involving head-up tilt (HUT) and graded lower body suction. This was performed at the subjects' resident altitude. Blood pressure (Portapres) and R-R interval (ECG) were recorded during the test, and spectral and cross-spectral analyses of heart period and systolic blood pressure time series were performed using data obtained both while supine and during HUT. The transfer function gain in the low-frequency range (LF, approximately 0.1 Hz) at the point of maximal coherence was used as a measure of cardiac baroreflex sensitivity (BRS). As previously reported, Peruvians displayed an unusually good orthostatic tolerance, while Ethiopians showed an orthostatic tolerance comparable to that of healthy sea-level residents. There were no significant differences between groups in the supine values of the spectral analysis results. Head-up tilt induced the expected changes in Ethiopians (an increase in the LF components and a decrease in the respiratory components) but not in Andeans. Cross-spectral analysis showed abnormal results from all groups of high-altitude dwellers. These results indicate that Ethiopians, but not Peruvians, behave similarly to sea-level residents in terms of orthostatic tolerance and autonomic responses to orthostatic stress, as assessed from spectral analyses, and this indicates good adaptation to their environment. However, in all the high-altitude groups the results of cross-spectral analysis were atypical, suggesting some degree of impairment in baroreflex function.

Hidden Markov Models Based on Symbolic Dynamics for Statistical Modeling of Cardiovascular Control in Hypertensive Pregnancy Disorders

Discrete hidden Markov models (HMMs) were applied to classify pregnancy disorders. The observation sequence was generated by transforming RR and systolic blood pressure time series using symbolic dynamics. Time series were recorded from 15 women with pregnancy-induced hypertension, 34 with preeclampsia and 41 controls beyond 30th gestational week. HMMs with five to ten hidden states were found to be sufficient to characterize different blood pressure variability, whereas significant classification in RR-based HMMs was found using fifteen hidden states. Pregnancy disorders preeclampsia and pregnancy induced hypertension revealed different patho-physiological autonomous regulation supposing different etiology of both disorders.

LONG-TERM CORRELATIONS AND FRACTAL DIMENSION OF BEAT-TO-BEAT BLOOD PRESSURE DYNAMICS

Arterial blood pressure is modulated by several physiological regulatory processes. The analysis of beat-to-beat blood pressure dynamics provides information about cardiovascular control and patho-physiological conditions. In this paper we investigated the long-term correlations and fractal dimension of systolic blood pressure time series applying detrended fluctuation analysis (DFA) and Higuchi's algorithm (HFD). Thirty-minute blood pressure recordings in 25 patients with dilated cardiomyopathy (DCM) and 27 healthy controls (CON) were analyzed. The DFA and HFD revealed multifractal features in the blood pressure dynamics of CON as well as of DCM. At small scales, DFA and HFD of CON were significantly different from those of CON, reflecting patho-physiological changes. In conclusion, scaling analysis of blood pressure dynamics might lead to an enhanced assessment of autonomic cardiovascular control in patients with DCM.

Smoothed pseudo Wigner–Ville distribution as an alternative to Fourier transform in rats

Techniques for examining signals in the time and frequency domains are well-established tools. These tools have their limitations; they tell us in a broad sense where the signal component exists in the frequency domain, but they do not tell us how its frequency characteristics change over time. The time-frequency has become a powerful alternative for the analysis of signals. Among various time-frequency distribution methods, one of the most studied is the Wigner–Ville distribution. The aim of this study was to evaluate in conscious rats smoothed pseudo Wigner–Ville distribution (SPWVD) as an alternative to the fast Fourier transform (FFT) in RR intervals and in systolic blood pressure (SBP), before and after adrenergic and cholinergic receptor blockade. Fourteen Wistar rats equipped with telemetry probe were evaluated: (1) under control conditions; (2) after injection of saline (100 μl kg −1 i.v.); (3) after atenolol (1 mg kg −1 i.v.); (4) after atropine methyl nitrate (0.5 mg kg −1 i.v.); and (5) after phentolamine (5 mg kg −1 i.v.). FFT and SPWVD were applied to RR intervals and SBP time series. Six-minute time series of RR intervals, systolic and diastolic pressures were analysed. The bias and distribution of differences between FFT and SPWVD methods in RR intervals under base conditions were 1.4±0.4% ( r 2=0.94; P<0.01) in LF/LF+HF; 1.5±0.5% ( r 2=0.92; P<0.01) in HF/LF+HF and 4.8±1.9% ( r 2=0.92; P<0.01) in LF/HF. In SBP the bias and distribution were 1.5±0.8% ( r 2=0.90; P<0.05) in LF/LF+HF and 1.7±0.6% ( r 2=0.92; P<0.01) in HF/LF+HF. In the frequency domain analysis of RR intervals and SBP there was no difference between FFT and SPWVD. The agreement between the methods demonstrates that in stationary signals both methods can be used interchangeably. SPWVD may be an interesting tool to analyse biomedical signals; it provides a good resolution at high frequency and a good frequency resolution at low frequencies independently if signals remain stationary.