Determination of Aortic Characteristic Impedance and Total Arterial Compliance From Regional Pulse Wave Velocities Using Machine Learning: An in-silico Study

A new methodology for determining the central pressure waveform from peripheral measurement using Fourier-based machine learning

Radial applanation tonometry is a well-established technique for hemodynamic monitoring and is becoming popular in affordable non-invasive wearable healthcare electronics. To assess the central aortic pressure using radial-based measurements, there is an essential need to develop mathematical approaches to estimate the central pressure waveform. In this study, we propose a new Fourier-based machine learning (F-ML) methodology to transfer non-invasive radial pressure measurements to the central pressure waveform. To test the method, collection of tonometry recordings of the radial and carotid pressure measurements are used from the Framingham Heart Study (2640 individuals, 55 % women) with mean (range) age of 66 (40-91) years. Method-derived estimates are significantly correlated with the measured ones for three major features of the pressure waveform (systolic blood pressure, r=0.97, p < 0.001; diastolic blood pressure, r=0.99, p < 0.001; and mean blood pressure, r=0.99, p < 0.001). In all cases, the Bland-Altman analysis shows negligible bias in the estimations and error is bounded to 5.4 mmHg. Findings also suggest that the F-ML approach reconstructs the shape of the central pressure waveform accurately with the average normalized root mean square error of 5.5 % in the testing population which is blinded to all stages of machine learning development. The results show that the F-ML transfer function outperforms the conventional generalized transfer function, particularly in terms of reconstructing the shape of the central pressure waveform morphology. The proposed F-ML transfer function can provide accurate estimates for the central pressure waveform, and ultimately expand the usage of non-invasive devices for central hemodynamic assessment.

Simultaneous Measurement of Local Pulse Wave Velocities in Radial Arteries Using a Soft Sensor Based on the Fiber Bragg Grating Technique

Arterial stiffness has been proved to be an important parameter in the evaluation of cardiovascular diseases, and Pulse Wave Velocity (PWV) is a strong indicator of arterial stiffness. Compared to regional PWV (PWV among different arteries), local PWV (PWV within a single artery) outstands in providing higher precision in indicating arterial properties, as regional PWVs are highly affected by multiple parameters, e.g., variations in blood vessel lengths due to individual differences, and multiple reflection effects on the pulse waveform. However, local PWV is less-developed due to its high dependency on the temporal resolution in synchronized signals with usually low signal-to-noise ratios. This paper presents a method for the noninvasive simultaneous measurement of two local PWVs in both left and right radial arteries based on the Fiber Bragg Grating (FBG) technique via correlation analysis of the pulse pairs at the fossa cubitalis and at the wrist. Based on the measurements of five male volunteers at the ages of 19 to 21 years old, the average left radial PWV ranged from 9.44 m/s to 12.35 m/s and the average right radial PWV ranged from 11.50 m/s to 14.83 m/s. What is worth mentioning is that a stable difference between the left and right radial PWVs was observed for each volunteer, ranging from 2.27 m/s to 3.04 m/s. This method enables the dynamic analysis of local PWVs and analysis of their features among different arteries, which will benefit the diagnosis of early-stage arterial stiffening and may bring more insights into the diagnosis of cardiovascular diseases.

Novel theory and potential applications of central diastolic pressure decay time constant

Central aortic diastolic pressure decay time constant ( τ ) is according to the two-element Windkessel model equal to the product of total peripheral resistance ( R ) times total arterial compliance ( C ). As such, it is related to arterial stiffness, which has considerable pathophysiological relevance in the assessment of vascular health. This study aimed to investigate the relationship of the constant τ with the product T MBP cPP , given by heart period ( T ) times the ratio of mean blood pressure (MBP) to central pulse pressure ( cPP ). The relationship was derived by performing linear fitting on an in silico population of n1 = 3818 virtual subjects, and was subsequently evaluated on in vivo data (n2 = 2263) from the large Asklepios study. The resulted expression was found to be τ = k ' T MBP cPP , withk ' = 0.7 (R2 = 0.9). The evaluation of the equation on the in vivo human data reported high agreement between the estimated and reference τ values, with a correlation coefficient equal to 0.94 and a normalized RMSE equal to 5.5%. Moreover, the analysis provided evidence that the coefficient k ' is age- and gender-independent. The proposed formula provides novel theoretical insights in the relationship between τ and central blood pressure features. In addition, it may allow for the evaluation of τ without the need for acquiring the entire central blood pressure wave, especially when an approximation of the cPP is feasible. This study adds to the current literature by contributing to the accessibility of an additional biomarker, such as the central diastolic pressure decay time constant, for the improved assessment of vascular ageing.

Arterial Stiffness: From Basic Primers to Integrative Physiology

The elastic properties of conductance arteries are one of the most important hemodynamic functions in the body, and data continue to emerge regarding the importance of their dysfunction in vascular aging and a range of cardiovascular diseases. Here, we provide new insight into the integrative physiology of arterial stiffening and its clinical consequence. We also comprehensively review progress made on pathways/molecules that appear today as important basic determinants of arterial stiffness, particularly those mediating the vascular smooth muscle cell (VSMC) contractility, plasticity and stiffness. We focus on membrane and nuclear mechanotransduction, clearance function of the vascular wall, phenotypic switching of VSMCs, immunoinflammatory stimuli and epigenetic mechanisms. Finally, we discuss the most important advances of the latest clinical studies that revisit the classical therapeutic concepts of arterial stiffness and lead to a patient-by-patient strategy according to cardiovascular risk exposure and underlying disease.

Variations of arterial compliance and vascular resistance due to plaque or infarct in a single vascular territory of the middle cerebral artery

Background

Arterial compliance (AC) and vascular resistance (VR) are crucial for the regulation capacity of the vascular system. However, alterations of these features and hemodynamics due to atherosclerosis in a single intracranial artery territory have not been extensively investigated. Thus this study aimed to examine the AC, VR, and hemodynamic variations due to plaque and infarction in the middle cerebral artery (MCA).

Methods

Patients with symptomatic MCA atherosclerosis were recruited. Both sides of the MCA were assessed and then classified according to the following scheme: group 0, without plaque; group 1, with plaque but without infarct; group 2, with plaque and infarct in the supplying territories. Data on AC, VR, blood flow, and pulsatility index (PI) were obtained based on 4D flow magnetic resonance imaging (MRI) and the Windkessel model.

Results

A total of 63 patients were recruited. After 17 MCAs were excluded (occlusion, n=6; poor image quality, n=11), datasets on 109 MCAs were finally collected and classified into group 0 (n=39), group 1 (n=40), and group 2 (n=30). From groups 0 to 2, there was a decrease in AC (0.0060±0.0031 vs. 0.0052±0.0029 vs. 0.0026±0.0020 mL/mmHg) and an increase in VR [28.65±16.11 vs. 42.59±27.53 vs. 63.21±40.37 mmHg/(mL/s)]. Compared to group 1, group 2 had significantly decreased AC (0.0052±0.0029 vs. 0.0026±0.0020 mL/mmHg; P=0.003) and increased VR [42.59±27.53 vs. 63.21±40.37 mmHg/(mL/s); P=0.021]. From group 0 to group 2, there was a decrease in blood flow (179.29±73.57 vs. 125.11±59.04 vs. 92.05±48.79 mL/min; P<0.001). The PI varied significantly among the 3 groups (0.86±0.20 vs. 1.12±0.50 vs. 0.79±0.16; P<0.001), with group 1 having the highest PI.

Conclusions

With the occurrence of plaque and infarct, AC and blood flow progressively decrease while VR increases. The PI was the highest in the group with plaque and without infarct. Assessments of vascular function and hemodynamics in a single artery territory can clarify comprehensive alterations in the cerebral vascular system (CVS).

Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats

Aims

Myocardial infarction (MI) is one of the leading causes of death worldwide. It is well accepted that early diagnosis followed by early reperfusion therapy significantly increases the MI survival. Diagnosis of acute MI is traditionally based on the presence of chest pain and electrocardiogram (ECG) criteria. However, around 50% of the MIs are without chest pain, and ECG is neither completely specific nor definitive. Therefore, there is an unmet need for methods that allow detection of acute MI or ischaemia without using ECG. Our hypothesis is that a hybrid physics-based machine learning (ML) method can detect the occurrence of acute MI or ischaemia from a single carotid pressure waveform.

Methods and results

We used a standard occlusion/reperfusion rat model. Physics-based ML classifiers were developed using intrinsic frequency parameters extracted from carotid pressure waveforms. ML models were trained, validated, and generalized using data from 32 rats. The final ML models were tested on an external stratified blind dataset from additional 13 rats. When tested on blind data, the best ML model showed specificity = 0.92 and sensitivity = 0.92 for detecting acute MI. The best model's specificity and sensitivity for ischaemia detection were 0.85 and 0.92, respectively.

Conclusion

We demonstrated that a hybrid physics-based ML approach can detect the occurrence of acute MI and ischaemia from carotid pressure waveform in rats. Since carotid pressure waveforms can be measured non-invasively, this proof-of-concept pre-clinical study can potentially be expanded in future studies for non-invasive detection of MI or myocardial ischaemia.

On the similarity between aortic and carotid pressure diastolic decay: a mathematical modelling study

Aortic diastolic pressure decay (DPD) has been shown to have considerable pathophysiological relevance in the assessment of vascular health, as it is significantly affected by arterial stiffening. Nonetheless, the aortic pressure waveform is rarely available and hence the utility of the aortic DPD is limited. On the other hand, carotid blood pressure is often used as a surrogate of central (aortic) blood pressure in cardiovascular monitoring. Although the two waveforms are inherently different, it is unknown whether the aortic DPD shares a common pattern with the carotid DPD. In this study, we compared the DPD time constant of the aorta (aortic RC) and the DPD time constant of the carotid artery (carotid RC) using an in-silico-generated healthy population from a previously validated one-dimensional numerical model of the arterial tree. Our results demonstrated that there is near-absolute agreement between the aortic RC and the carotid RC. In particular, a correlation of ~ 1 was reported for a distribution of aortic/carotid RC values equal to 1.76 ± 0.94 s/1.74 ± 0.87 s. To the best of our knowledge, this is the first study to compare the DPD of the aortic and the carotid pressure waveform. The findings indicate a strong correlation between carotid DPD and aortic DPD, supported by the examination of curve shape and the diastolic decay time constant across a wide range of simulated cardiovascular conditions. Additional investigation is required to validate these results in human subjects and assess their applicability in vivo.

Mock circulatory loop generated database for dynamic characterization of pressure-based cardiac output monitoring systems

Pulse contour cardiac output monitoring systems allow real-time and continuous estimation of hemodynamic variables such as cardiac output (CO) and stroke volume variation (SVV) by analysis of arterial blood pressure waveforms. However, evaluating the performance of CO monitoring systems to measure the small variations in these variables sometimes used to guide fluid therapy is a challenge due to limitations in clinical reference methods. We developed a non-clinical database as a tool for assessing the dynamic attributes of pressure-based CO monitoring systems, including CO response time and CO and SVV resolutions. We developed a mock circulation loop (MCL) that can simulate rapid changes in different parameters, such as CO and SVV. The MCL was configured to simulate three different states (normovolemic, cardiogenic shock, and hyperdynamic) representing a range of flow and pressure conditions. For each state, we simulated stepwise changes in the MCL flow and collected datasets for characterizing pressure-based CO systems. Nine datasets were generated that contain hours of peripheral pressure, central flow and pressure waveforms. The MCL-generated database is provided open access as a tool for evaluating dynamic characteristics of pressure-based CO algorithms and systems in detecting variations in CO and SVV indices. In an example application of the database, a CO response time of 10 s, CO and SVV resolutions with lower and upper limits of (-9.1%, 8.4%) and (-5.0%, 3.8%), respectively, were determined for a pressure-based CO benchtop system. This tool will support a more comprehensive assessment of pressure-based CO monitoring systems and algorithms.

First blood: An efficient, hybrid one- and zero-dimensional, modular hemodynamic solver

Low-dimensional (1D or 0D) models can describe the whole human blood circulation, for example, 1D distributed parameter model for the arterial network and 0D concentrated models for the heart or other organs. This paper presents a combined 1D-0D solver, called first_blood, that solves the governing equations of fluid dynamics to model low-dimensional hemodynamic effects. An extended method of characteristics is applied here to solve the momentum, and mass conservation equations and the viscoelastic wall model equation, mimicking the material properties of arterial walls. The heart and the peripheral lumped models are solved with a general zero-dimensional (0D) nonlinear solver. The model topology can be modular, that is, first_blood can solve any 1D-0D hemodynamic model. To demonstrate the applicability of first_blood, the human arterial system, the heart and the peripherals are modelled using the solver. The simulation time of a heartbeat takes around 2 s, that is, first_blood requires only twice the real-time for the simulation using an average PC, which highlights the computational efficiency. The source code is available on GitHub, that is, it is open source. The model parameters are based on the literature suggestions and on the validation of output data to obtain physiologically relevant results.

Wave Intensity Analysis Combined With Machine Learning can Detect Impaired Stroke Volume in Simulations of Heart Failure

Heart failure is treatable, but in the United Kingdom, the 1-, 5- and 10-year mortality rates are 24.1, 54.5 and 75.5%, respectively. The poor prognosis reflects, in part, the lack of specific, simple and affordable diagnostic techniques; the disease is often advanced by the time a diagnosis is made. Previous studies have demonstrated that certain metrics derived from pressure-velocity-based wave intensity analysis are significantly altered in the presence of impaired heart performance when averaged over groups, but to date, no study has examined the diagnostic potential of wave intensity on an individual basis, and, additionally, the pressure waveform can only be obtained accurately using invasive methods, which has inhibited clinical adoption. Here, we investigate whether a new form of wave intensity based on noninvasive measurements of arterial diameter and velocity can detect impaired heart performance in an individual. To do so, we have generated a virtual population of two-thousand elderly subjects, modelling half as healthy controls and half with an impaired stroke volume. All metrics derived from the diameter-velocity-based wave intensity waveforms in the carotid, brachial and radial arteries showed significant crossover between groups-no one metric in any artery could reliably indicate whether a subject's stroke volume was normal or impaired. However, after applying machine learning to the metrics, we found that a support vector classifier could simultaneously achieve up to 99% recall and 95% precision. We conclude that noninvasive wave intensity analysis has significant potential to improve heart failure screening and diagnosis.