Radial Blood Pressure Waveform Research Articles

Central Aortic Blood Pressure Waveform Estimation with a Temporal Convolutional Network.

A novel temporal convolutional network (TCN) model is utilized to reconstruct the central aortic blood pressure (aBP) waveform from the radial blood pressure waveform. The method does not need manual feature extraction as traditional transfer function approaches. The data acquired by the SphygmoCor CVMS device in 1,032 participants as a measured database and a public database of 4,374 virtual healthy subjects were used to compare the accuracy and computational cost of the TCN model with the published convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model. The TCN model was compared with CNN-BiLSTM in the root mean square error (RMSE). The TCN model generally outperformed the existing CNN-BiLSTM model in terms of accuracy and computational cost. For the measured and public databases, the RMSE of the waveform using the TCN model was 0.55 ± 0.40 mmHg and 0.84 ± 0.29 mmHg, respectively. The training time of the TCN model was 9.63 min and 25.51 min for the entire training set; the average test time was around 1.79 ms and 8.58 ms per test pulse signal from the measured and public databases, respectively. The TCN model is accurate and fast for processing long input signals, and provides a novel method for measuring the aBP waveform. This method may contribute to the early monitoring and prevention of cardiovascular disease.

Cardiac output estimated from an uncalibrated radial blood pressure waveform: validation in an in-silico-generated population.

Background: Cardiac output is essential for patient management in critically ill patients. The state-of-the-art for cardiac output monitoring bears limitations that pertain to the invasive nature of the method, high costs, and associated complications. Hence, the determination of cardiac output in a non-invasive, accurate, and reliable way remains an unmet need. The advent of wearable technologies has directed research towards the exploitation of wearable-sensed data to improve hemodynamical monitoring. Methods: We developed an artificial neural networks (ANN)-enabled modelling approach to estimate cardiac output from radial blood pressure waveform. In silico data including a variety of arterial pulse waves and cardiovascular parameters from 3,818 virtual subjects were used for the analysis. Of particular interest was to investigate whether the uncalibrated, namely, normalized between 0 and 1, radial blood pressure waveform contains sufficient information to derive cardiac output accurately in an in silico population. Specifically, a training/testing pipeline was adopted for the development of two artificial neural networks models using as input: the calibrated radial blood pressure waveform (ANNcalradBP), or the uncalibrated radial blood pressure waveform (ANNuncalradBP). Results: Artificial neural networks models provided precise cardiac output estimations across the extensive range of cardiovascular profiles, with accuracy being higher for the ANNcalradBP. Pearson's correlation coefficient and limits of agreement were found to be equal to [0.98 and (-0.44, 0.53)L/min] and [0.95 and (-0.84, 0.73)L/min] for ANNcalradBP and ANNuncalradBP, respectively. The method's sensitivity to major cardiovascular parameters, such as heart rate, aortic blood pressure, and total arterial compliance was evaluated. Discussion: The study findings indicate that the uncalibrated radial blood pressure waveform provides sample information for accurately deriving cardiac output in an in silico population of virtual subjects. Validation of our results using in vivo human data will verify the clinical utility of the proposed model, while it will enable research applications for the integration of the model in wearable sensing systems, such as smartwatches or other consumer devices.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels.

Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3. The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals. Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups. The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

Discrimination between possible sarcopenia and metabolic syndrome using the arterial pulse spectrum and machine-learning analysis

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.

Machine-Learning Classification of Pulse Waveform Quality.

Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin-sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin-surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.

Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech COVID-19 Vaccine (BNT162b2).

Various adverse events and complications have been attributed to COVID-19 (coronavirus disease 2019) vaccinations, which can affect the cardiovascular system, with conditions such as myocarditis, thrombosis, and ischemia. The aim of this study was to combine noninvasive pulse measurements and frequency domain analysis to determine if the Pfizer-BioNTech COVID-19 vaccine (BNT162b2) vaccination and its accompanying cardiovascular side effects will induce changes in arterial pulse transmission and waveform. Radial blood pressure waveform and photoplethysmography signals were measured noninvasively for 1 min in 112 subjects who visited Shuang-Ho Hospital for a BNT162b2 vaccination. Based on side effects, each subject was assigned to Group N (no side effects), Group CV (cardiac or vascular side effects), Group C (cardiac side effects only), or Group V (vascular side effects only). Two classification methods were used: (1) machine-learning (ML) analysis using 40 harmonic pulse indices (amplitude proportions, phase angles, and their variability indices) as features, and (2) a pulse-variability score analysis developed in the present study. Significant effects on the pulse harmonic indices were noted in Group V following vaccination. ML and pulse-variability score analyses provided acceptable AUCs (0.67 and 0.80, respectively) and hence can aid discriminations among subjects with cardiovascular side effects. When excluding ambiguous data points, the AUC of the score analysis further improved to 0.94 (with an adopted proportion of around 64.1%) for vascular side effects. The present findings may help to facilitate a time-saving and easy-to-use method for detecting changes in the vascular properties associated with the cardiovascular side effects following BNT162b2 vaccination.

CENTRAL BLOOD PRESSURE ESTIMATION IN TYPE 1 DIABETES: IMPACT OF PERIPHERAL BLOOD PRESSURE CALIBRATION APPROACH

Objective: Pulse wave analysis (PWA) of peripheral arterial pressure waveforms is used for non-invasive estimation of central blood pressure (BP), which can assist in cardiovascular (CV) risk assessment in populations with a high CV burden such as patients with type 1 diabetes (T1DM). However, correct calibration of peripheral BP waveforms is important in order to accurately estimate central BP. The impact of peripheral BP waveform calibration on central BP is not investigated yet in T1DM. Therefore, this study aims first for the first time in a T1DM patient cohort, to examine differences in central BP parameters estimated by PWA of radial BP waveforms, depending on which brachial BP (SBP/DBP vs. DBP/MAP) is used for calibration of the radial BP waveforms. Design and method: Cross-sectional study, in which adult T1DM patients without known CVD were enrolled. Radial artery BP waveforms were acquired using applanation tonometry (SphygmoCorâ) for the estimation of central systolic BP (central SBP), central pulse pressure (central PP) and central augmentation pressure (central AP), using either brachial SBP/DBP or DBP/MAP for calibration of the radial pressure waveforms. Brachial MAP was calculated using a fixed form factor of 40% (MAP = brachial DBP + 0.40 × brachial PP). Results: 54 patients (M/F: 32/22; age: 46 ± 9.5 yrs; T1DM duration: 27 ± 8.8 yrs) were evaluated. All three estimated central BP parameters were significantly higher when brachial DBP/MAP was used compared to SBP/DBP calibration (7.5 ± 5.04 mmHg, 7.5 ± 5.04 mmHg and 1.5 ± 1.36 mmHg higher central SBP, PP and AP, respectively, p < 0.001). Brachial-to-radial SBP amplification was estimated at 10 ± 7.1 mmHg (p < 0.001). Conclusions: This study confirms for the first time that in patients with T1DM there are significant differences in central BP parameters estimated with PWA, depending on the method used for calibration of the radial pressure waveforms. Brachial DBP/MAP calibration resulted in consistently higher central BP parameters as compared to using SBP/DBP. Hence, it seems that brachial-to-radial SBP amplification is important to take into account in the estimation of central BP parameters also in patients with T1DM.

Classification of patients with Alzheimer\u2019s disease using the arterial pulse spectrum and a multilayer-perceptron analysis

Cerebrovascular atherosclerosis has been identified as a prominent pathological feature of Alzheimer’s disease (AD); the link between vessel pathology and AD risk may also extend to extracranial arteries. This study aimed to determine the effectiveness of using arterial pulse-wave measurements and multilayer perceptron (MLP) analysis in distinguishing between AD and control subjects. Radial blood pressure waveform (BPW) and finger photoplethysmography signals were measured noninvasively for 3 min in 87 AD patients and 74 control subjects. The 5-layer MLP algorithm employed evaluated the following 40 harmonic pulse indices: amplitude proportion and its coefficient of variation, and phase angle and its standard deviation. The BPW indices differed significantly between the AD patients (6247 pulses) and control subjects (6626 pulses). Significant intergroup differences were found between mild, moderate, and severe AD (defined by Mini-Mental-State-Examination scores). The hold-out test results indicated an accuracy of 82.86%, a specificity of 92.31%, and a 0.83 AUC of ROC curve when using the MLP-based classification between AD and Control. The identified differences can be partly attributed to AD-induced changes in vascular elastic properties. The present findings may be meaningful in facilitating the development of a noninvasive, rapid, inexpensive, and objective method for detecting and monitoring the AD status.

A novel electrical impedance function to estimate central aortic blood pressure waveforms

Background and objectiveCentral aortic blood pressure waveforms convey imperative information about cardiovascular risk, but these measurements are obtained invasively. The study aims to develop an electrical impedance function (EIF) to estimate the central (aortic) blood pressure waveform from the radial blood pressure waveform without the need of invasive procedures. MethodsThis paper shows a method of using the four-element Windkessel model to derive an EIF using circuit analysis to estimate the central aortic blood pressure. The four elements were identified from radial waveforms by using the Nelder-Mead simplex algorithm and then used to construct the EIF to obtain the aortic pressure waveform, given the radial waveform. ResultsWaveforms generated by EIF gave the lowest values for Root Mean Square Error (RMSE) (5.69) and Mean Average Percentage Error (MAPE) (0.0402) compared to the generalized transfer function (GTF) and N-point moving average (NPMA), and the lowest MAPE and a comparable RMSE when compared to the Adaptive Transfer Function (ATF). Moreover, EIF has a significantly lower computing time (0.008 ms) when compared with GTF, NPMA and ATF. ConclusionsThe proposed function had comparable or better performance to existing methods when tested on both simulated and actual patient data, and was able to give a better signal shape retention at the dicrotic notch (99.94 % correlation), which is a crucial feature for cardiovascular disease detection. SignificanceOverall the EIF performance validates its use in estimating central aortic pressure waveforms, making it potentially viable for better embedded systems for non-invasive central blood pressure monitoring due to its low computation time and high accuracy.

Characteristics of pulse-waveform and laser-Doppler indices in frozen-shoulder patients

This study tested the hypothesis that measuring and analyzing the arterial pulse waveform and the skin-surface blood flow makes it possible to noninvasively discriminate the different microcirculatory states of patients with frozen shoulder (FS). Radial blood pressure waveform (BPW), finger photoplethysmography (PPG), and skin-surface laser Doppler flowmetry (LDF) signals were measured noninvasively on the back of the hand in 25 FS and 18 control subjects. Beat-to-beat, spectral, and variability analyses were applied to the 3-minute-long recorded signals. Significant intergroup differences were found in the BPW, PPG, and LDF indices. For example, the amplitude indices of the predominant (lower-frequency) BPW components were significantly larger in FS subjects than controls. Some of the PPG phase-angle variability indices were significantly larger on the diseased side than on the contralateral side. The present results illustrate that LDF indices can be used to evaluate the blood-flow-perfusion responses and their regulation, and that pulse-waveform indices can help to evaluate changes in the arterial pulse-wave transmission condition and its regulation in FS. Moreover, the trends in the changes in certain spectral pulse-waveform indices were similar for the wrist BPW and finger PPG signals. These findings could facilitate the development of a rapid, inexpensive, and objective technique for evaluating the blood-flow responses induced by FS.

Evaluation of the Linear Relationship Between Pulse Arrival Time and Blood Pressure in ICU Patients: Potential and Limitations.

A variety of techniques based on the indirect measurement of blood pressure (BP) by Pulse Transit Time (PTT) have been explored over the past few years. Such an approach has the potential in providing continuous and non-invasive beat to beat blood pressure without the use of a cuff. Pulse Arrival Time (PAT) which includes the cardiac pre-ejection period has been proposed as a surrogate of PTT, however, the balance between its questioned accuracy and measurement simplicity has yet to be established. The present work assessed the degree of linear relationship between PAT and blood pressure on 96 h of continuous electrocardiography and invasive radial blood pressure waveforms in a group of 11 young ICU patients. Participants were selected according to strict exclusion criteria including no use of vasoactive medications and presence of clinical conditions associated with cardiovascular diseases. The average range of variation for diastolic BP was 60 to 79 mmHg while systolic BP varied between 123 and 158 mmHg in the study database. The overall Pearson correlation coefficient for systolic and diastolic blood pressure was −0.5 and −0.42, respectively, while the mean absolute error was 3.9 and 7.6 mmHg. It was concluded that the utilization of PAT for the continuous non-invasive blood pressure estimation is rather limited according to the experimental setup, nonetheless the correlation coefficient performed better when the range of variation of blood pressure was high over periods of 30 min suggesting that PAT has the potential to be used as indicator of changes relating to hypertensive or hypotensive episodes.

A Simple Adaptive Transfer Function for Deriving the Central Blood Pressure Waveform from a Radial Blood Pressure Waveform.

Generalized transfer functions (GTFs) are available to compute the more relevant central blood pressure (BP) waveform from a more easily measured radial BP waveform. However, GTFs are population averages and therefore may not adapt to variations in pulse pressure (PP) amplification (ratio of radial to central PP). A simple adaptive transfer function (ATF) was developed. First, the transfer function is defined in terms of the wave travel time and reflection coefficient parameters of an arterial model. Then, the parameters are estimated from the radial BP waveform by exploiting the observation that central BP waveforms exhibit exponential diastolic decays. The ATF was assessed using the original data that helped popularize the GTF. These data included radial BP waveforms and invasive reference central BP waveforms from cardiac catheterization patients. The data were divided into low, middle, and high PP amplification groups. The ATF estimated central BP with greater accuracy than GTFs in the low PP amplification group (e.g., central systolic BP and PP root-mean-square-errors of 3.3 and 4.2 mm Hg versus 6.2 and 7.1 mm Hg; p ≤ 0.05) while showing similar accuracy in the higher PP amplification groups. The ATF may permit more accurate, non-invasive central BP monitoring in elderly and hypertensive patients.

Optimization of a generalized radial-aortic transfer function using parametric techniques

The central aortic blood pressure (cBP) waveform, which is different to that of peripheral locations, is a clinically important parameter for assessing cardiovascular function, however the gold standard for measuring cBP involves invasive catheter-based techniques. The difficulties associated with invasive measurements have given rise to the development of a variety of noninvasive methods. An increasingly applied method for the noninvasive derivation of cBP involves the application of transfer function (TF) techniques to a non-invasively measured radial blood pressure (BP) waveform. The purpose of the current study was to investigate the development of a general parametric model for determination of cBP from tonometrically transduced radial BP waveforms. The study utilized simultaneously measured invasive central aortic and noninvasive radial BP waveform measurements. Data sets were available from 92 subjects, a large cohort for a study of this nature. The output error (OE) model was empirically identified as the most appropriate model structure. A generalized model was developed using a pre-specified derivation cohort and then applied to a validation data set to estimate the recognized features of the cBP waveform. While our results showed that many relevant BP parameters could be derived within acceptable limits, the estimated augmentation index (AI) displayed only a weak correlation compared to the invasively measured value, indicating that any clinical diagnosis or interpretation based on estimated AI should be undertaken with caution.

Excess Pressure Integral Predicts Cardiovascular Events Independent of Other Risk Factors in the Conduit Artery Functional Evaluation Substudy of Anglo-Scandinavian Cardiac Outcomes Trial

Excess pressure integral (XSPI), a new index of surplus work performed by the left ventricle, can be calculated from blood pressure waveforms and may indicate circulatory dysfunction. We investigated whether XSPI predicted future cardiovascular events and target organ damage in treated hypertensive individuals. Radial blood pressure waveforms were acquired by tonometry in 2069 individuals (aged, 63±8 years) in the Conduit Artery Functional Evaluation (CAFE) substudy of the Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT). Measurements of left ventricular mass index (n=862) and common carotid artery intima media thickness (n=923) were also performed. XSPI and the integral of reservoir pressure were lower in people treated with amlodipine±perindopril than in those treated with atenolol±bendroflumethiazide, although brachial systolic blood pressure was similar. A total of 134 cardiovascular events accrued during a median 3.4 years of follow-up; XSPI was a significant predictor of cardiovascular events after adjustment for age and sex, and this relationship was unaffected by adjustment for conventional cardiovascular risk factors or Framingham risk score. XSPI, central systolic blood pressure, central augmentation pressure, central pulse pressure, and integral of reservoir pressure were correlated with left ventricular mass index, but only XSPI, augmentation pressure, and central pulse pressure were associated positively with carotid artery intima media thickness. Associations between left ventricular mass index, XSPI, and integral of reservoir pressure and carotid artery intima media thickness and XSPI were unaffected by multivariable adjustment for other covariates. XSPI is a novel indicator of cardiovascular dysfunction and independently predicts cardiovascular events and targets organ damage in a prospective clinical trial.

Effects of different contacting pressure on the transfer function between finger photoplethysmographic and radial blood pressure waveforms

The blood pressure (BP) waveform is suggested to reflect the whole-body blood supply distribution, but its non-invasive assessment is not sufficiently user friendly for practical applications. The present authors studied the correlation between BP and photoplethysmography (PPG) waveforms, with the aim of determining the optimal range for contact pressure stimulation (PS) to produce a reliable transfer function in their harmonic parameters. Finger PPG and radial-artery BP signals were measured simultaneously and noninvasively on healthy volunteers (n = 45). PS of 0-200 mmHg was applied to the finger, and 1 min data sequences were recorded. In frequency-domain analysis, linear regression was applied to the calculated amplitude ratios or the first five harmonics between BP and PPG waveforms. In the 60 mmHg-PS group, the BP-PPG regression of amplitude ratios was highest, and the agreements between them were also the best verified by Bland-Altman analysis. In the present study, frequency-domain analysis was performed to study the correlation between BP and PPG waveforms. Differences in pressure-induced-vasodilation responses underlie the different BP-PPG waveform correlations obtained by applying different PSs. The non-invasively derived PPG parameters might help to provide an easier method to acquire the radial-artery BP waveform, and hence broaden the application of BP waveform analysis.

Application of Fourier domain arterial transfer functions for the estimation of aortic waveform characteristics from radial blood pressure waveforms

Application of Fourier domain arterial transfer functions for the estimation of aortic waveform characteristics from radial blood pressure waveforms

A comparison of gender specific and generalized arterial transfer functions for the derivation of aortic waveform characteristics from radial blood pressure waveforms

A comparison of gender specific and generalized arterial transfer functions for the derivation of aortic waveform characteristics from radial blood pressure waveforms

A comparison of pressure and time characteristics between aortic and radial blood pressure waveforms

A comparison of pressure and time characteristics between aortic and radial blood pressure waveforms

Study of arterial blood pressure by a Windkessel-type model: influence of arterial functional properties

Objective: to analyse the performance of a Windkessel blood pressure (BP) modeling of arterial compliance adjusted in a dynamic fashion according to a non-linear relationship between the arterial compliance (AC) and BP. Non invasive measurements of the radial BP waveform (MILLAR tonometry) were compared to those constructed by an electric simulator reproducing the model in a symmetrical network subdivided into 121 segments. We introduced at cardiac level the aortic stroke volume (Doppler echocardiography) and the dynamic values of compliance (relation of compliance-to pressure, constant or variable) whether the model was linear or non linear, measured by high resolution Doppler (NIUS 02) for each subject. Results: at the radial artery segment the modelled BP obtained by the non linear model of AC was not significantly different from the measured BP wave, while in the linear model (AC constant at mean BP level) the systolic BP was significantly underestimated. (* P<0.05) Conclusion: this work shows the limits inherent in simplification of arterial compliance in the Windkessel model using constant parameters. This demonstrates the influence of the dynamic properties of the arterial wall in a conduction artery on the level of systolic and diastolic BP.