Estimation Of Pressure Research Articles

Maternal cell-free DNA in early pregnancy for preeclampsia screening: a systematic review.

To quantify the separation between maternal blood cell-free (cf)DNA markers in preeclampsia and unaffected pregnancies and compare with existing markers. This approach has not been used in previous studies. Comprehensive systematic literature search of PubMed to identify studies measuring total cfDNA, fetal cf(f)DNA or the fetal fraction (FF) in pregnant women. Included-studies of asymptomatic pregnancies with subsequent preeclampsia (cases) and unaffected pregnancies (controls) tested in the first or second trimester and before the clinical onset of preeclampsia. Excluded-studies not reporting the median or mean, standard deviation, inter-quartile range or range in cases and controls. Information from 26 eligible studies was entered into a meta-analysis to estimate, for each marker, the Mahalanobis distance, a measure of separation between the overlapping distributions in preeclampsia and unaffected pregnancies. This was compared with estimates for mean arterial pressure (MAP), uterine artery Doppler pulsatility index (UtA-PI), pregnancy associated plasma protein (PAPP)-A and placental growth factor (PlGF). The mean Mahalanobis distance for total cfDNA was 0.44 (95% CI 0.12-0.76), which fell between UtA-PI (0.53) and the absolute value of PAPP-A (-0.36). For cffDNA the distance was 1.03 (0.37-1.69), which is superior to MAP (0.74), UtA-PI, PlGF (-0.57) and PAPP-A. The distance for FF was -0.34 (-0.56 to -0.12), similar to PAPP-A. All three markers have a potential preeclampsia screening role, particularly cffDNA. However, to estimate the screening performance in combination with existing markers further large studies are needed. The current analysis will help in the power calculation for such studies.

Solid-state esophageal pressure sensor for the estimation of pleural pressure: a bench and first-in-human validation study

BackgroundAdvanced respiratory monitoring through the measurement of esophageal pressure (Pes) as a surrogate of pleural pressure helps guiding mechanical ventilation in ICU patients. Pes measurement with an esophageal balloon catheter, the current clinical reference standard, needs complex calibrations and a multitude of factors influence its reliability. Solid-state pressure sensors might be able to overcome these limitations.ObjectivesTo evaluate the accuracy of a new solid-state Pes transducer (Pessolid). We hypothesized that measurements are non-inferior to those obtained with a properly calibrated balloon catheter (Pesbal).MethodsAbsolute and relative solid-state sensor Pes measurements were compared to a reference pressure in a 5-day bench setup, and to simultaneously placed balloon catheters in 15 spontaneously breathing healthy volunteers and in 16 mechanically ventilated ICU patients. Bland–Altman analysis was performed using mixed effects modelling with bootstrapping to estimate bias and upper and lower limits of agreement (LoA) and their confidence intervals.ResultsBench study: Solid-state pressure transducers had a positive bias (Psolid – Pref) of around 1 cmH2O for the absolute minimal and maximum pressures, and no bias for pressure swings. Healthy volunteers: the solid-state transducer revealed a bias (i.e., Pessolid – Pesbal) [upper LoA; lower LoA] of 1.59 [8.21; − 5.02], − 2.32 [4.27; − 8.92] and 3.91 [11.04; − 3.23] cmH2O for end-expiratory, end-inspiratory and ΔPes values, respectively. ICU patients: the solid-state transducer showed a bias (Pessolid–Pesbal) [upper LoA; lower LoA] during controlled/assisted ventilation of: − 0.15 [1.41; − 1.72]/− 0.19 [5.23; − 5.62], 0.32 [3.45; − 2.82]/− 0.54 [4.81; − 5.90] and 0.47 [3.90; − 2.96]/0.35 [4.01; − 3.31] cmH2O for end-expiratory, end-inspiratory and ΔPes values, respectively. LoA were ≤ 2cmH2O for static measurements on controlled ventilation.ConclusionsThe novel solid-state pressure transducer showed good accuracy on the bench, in healthy volunteers and in ventilated ICU-patients. This could contribute to the implementation of Pes as advanced respiratory monitoring technique.Trial registrationClinicaltrials.gov identifier: NCT05817968 (patient study). Registered on 18 April 2023.

Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms

Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation. Data from 112 neurocritical patients with acute brain injuries were used, with 92 patients randomly assigned to training and testing, and 20 reserved for independent validation. The ML model achieved a mean absolute error of 3.00 mmHg, with a 95% confidence interval within ±7.5 mmHg. Approximately 72% of estimates from the validation sample were within 0-4 mmHg of invasive ICP values. This proof-of-concept study demonstrates that noninvasive ICP estimation via the B4C System and ML is feasible. Prospective studies are needed to validate the model’s clinical utility across diverse settings.

THE APPLICATION OF PLASTIC FIBER OPTIC SENSOR AS BLOOD PRESSURE MONITORING

Continuous blood pressure monitoring is essential for early hypertension prevention and cardiovascular disease diagnosis. Traditional methods are unsuitable for long-term use due to discomfort and limited portability. This study presents a tapered plastic fiber optical sensor (PFOS) as a sustainable, non-invasive solution for continuous monitoring. The PFOS system employs a light modulator based on mechanical waves to detect arterial pressure changes, utilizing an infrared light source (940 nm). The cuffless design includes four configurations: Bend, Bend with 1 Scratch, Bend with 3 Scratches, and Straight with 3 Scratches. The Bend with 1 Scratch configuration demonstrated superior performance, achieving 99.84% accuracy, a mean absolute error (MAE) of 0.1564, a linearity of 0.9999, and a sensitivity of 2.9997 Hz/dBm. Experimental validation involved testing radial and brachial arteries. Blood pressure estimates from Pulse Transit Time (PTT) were compared to a standard sphygmomanometer. On the radial artery, the Bend with 1 Scratch configuration achieved the best results, with the lowest MAE (1.72 for SBP, 2.39 for DBP) and highest accuracy (98.30% for SBP, 96.56% for DBP). The Straight with 3 Scratches configuration performed best on the brachial artery, with an MAE of 2.81 for SBP and 5.11 for DBP, and accuracies of 97.21% for SBP and 92.67% for DBP. The PFOS system offers a promising option for continuous monitoring in clinical and home settings.

Position statement on the use of point-of-care Ultrasound in Heart Failure: Recommendations from the Heart Failure and Atrial Fibrillation, and Clinical Ultrasound Working Groups of the Spanish Society of Internal Medicine (SEMI).

This document provides a comprehensive analysis of the use of point-of-care ultrasound in heart failure (HF), offering detailed recommendations on echocardiography, lung ultrasound, and venous ultrasound. These advanced imaging techniques allow for an accurate, detailed, and non-invasive evaluation of heart failure, facilitating rapid and effective clinical decision-making. Echocardiography enables a rapid assessment of cardiac function at the point of care, enhancing traditional physical examination and being essential for the management of heart failure (HF). Lung ultrasound provides a detailed view of the pulmonary status, crucial for diagnosing pulmonary congestion without using ionizing radiation, ideal for continuous monitoring. Venous ultrasound evaluates systemic congestion by measuring the inferior vena cava and other parameters, aiding in the estimation of right atrial pressure and monitoring pressure and volume overload, improving patient understanding and prognosis. Together, the use of these ultrasound modalities not only complements but enriches the traditional physical examination, establishing themselves as indispensable tools in the comprehensive and effective management of patients with heart failure. By incorporating these techniques into clinical practice, healthcare professionals can achieve better evaluation, diagnosis, and treatment, resulting in more personalized management of these patients.

Utilizing gene expression programming strategies for estimating relative uplift pressure head at key-points beneath straight horizontal aprons

ABSTRACT Seepage flow beneath the hydraulic structures creates uplift pressure on its floor that affects the performance of these structures. Prior studies have assumed that the cutoff wall is solid, however, perfect impermeability does not occur in the real world. This investigation yields an acceptable model for computing the relative uplift pressure at key points created by a vertical cutoff wall positioned at different locations (at the upstream end, at an intermediate location, and at the downstream end) underneath hydraulic structures. A series of numerical models based on the finite element method (FEM) were developed. With the generated data, Gene Expression Programming (GEP) is used to calculate the relative uplift pressure. The GEP model yielded high values of R 2, R, KGE, and NSE, and low values of MAE, RE%, and RMSE. Additionally, diagrams that allowed better estimation of relative uplift pressure were created. The NSE and KGE indices extracted using the GEP model are in a range of 0.75 < NSE ≤ 1 and 0.7 < KGE ≤ 1 whereas the RE% for the proposed models for each of the key points is less than 7.5%.

Imaging-based method to quantify left ventricular diastolic pressures.

To establish an imaging-based method to quantify left ventricular (LV) diastolic pressures. In 115 patients suspected of coronary artery disease, LV pressure was measured by micromanometers and images by echocardiography. LV filling pressure was measured as LV pre-atrial contraction pressure (pre-A PLV). Based on previous observations we hypothesized that pre-A PLV approximates the sum of minimum PLV and maximum transmitral pressure difference. Parameters used for pressure estimates included LV volumes and strain, left atrial strain, mitral flow velocities, systolic arterial cuff pressure and body mass index. Minimum PLV was estimated by predictors identified in a derivative cohort (n=81). Mitral pressure difference was calculated by a simplified Navier-Stokes equation. Accuracy of estimates of minimum PLV, pre-A PLV and end-diastolic PLV were investigated in a testing cohort (n=19). Patient-specific LV diastolic pressure curves were constructed by adjusting a reference curve according to pressure estimates at key diastolic events.There was good agreement between estimated and measured pre-A PLV: Bias 0.0, limits of agreement <3.1 mmHg (±1.96SD). Estimated minimum PLV and end-diastolic PLV also showed good agreement with measured pressures. Furthermore, there was good agreement between measured and estimated LV diastolic pressure curves, quantified as mean LV diastolic pressure: Bias 0.2, limits of agreement <3.2 mmHg. The proposed non-invasive method provided estimates of minimum PLV, pre-A PLV and end-diastolic PLV, each reflecting different features of diastolic function. Additionally, it provided an estimate of the LV diastolic pressure curve. Validation in larger populations with different phenotypes is necessary to determine the validity of the method in clinical practice.

An Upscaling Machine Learning Method Based On Physical Information

Objective: The objective of this study is to investigate a machine learning methodology based on the physics of dynamic fluid flow, with the aim of incorporating the knowledge of physical laws into learning algorithms to estimate precise results with lower computational cost. Theoretical Framework: This section presents the main concepts and theories that underpin the research. Key theories include nonlinear partial differential equations (PDEs), Darcy's Law, and the conservation of mass and energy, providing a solid foundation for understanding the research context. Method: The methodology adopted for this research involves modeling a one-dimensional two-phase fluid flow problem in a porous medium, governed by a first-order hyperbolic nonlinear equation. Data collection was conducted through numerical simulations, using micro and macro grids to evaluate permeability and boundary conditions. Results and Discussion: The results revealed that considering learning only in absolute permeability yielded good estimates of reservoir pressures. However, small discrepancies were observed in the estimation of saturations. The discussion contextualizes these results in light of the theoretical framework, highlighting the identified implications and relationships, as well as the study's limitations. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of reservoir modeling and fluid flow simulations. These implications may include optimizing oil extraction processes and improving predictive models in reservoir engineering. Originality/Value: This study contributes to literature by presenting an innovative approach that integrates physical laws into machine learning algorithms, resulting in more accurate and efficient estimates. The relevance and value of this research are evidenced by the potential application of the results in optimizing industrial processes and improving computational simulations.

Assessing Air-Pocket Pressure Peaks During Water Filling Operations Using Dimensionless Equations

Air pockets can become trapped at high points in pipelines with irregular profiles, particularly during service interruptions. The resulting issues, primarily caused by peak pressures generated during pipeline filling, are a well-documented topic in the literature. However, it is surprising that this subject has not received comprehensive attention. Using a model developed by the authors, this paper identifies the key parameters that define the phenomenon, presenting equations in a dimensionless format. The main advantage of this study lies in the ability to easily compute pressure surges without the need to solve a complex system of differential and algebraic equations. Numerous cases of filling operations were analysed to obtain dimensionless charts that can be used by water utilities to compute pressure surges during filling operations. Additionally, it provides charts that facilitate the rapid and reasonably accurate estimation of peak pressures. Depending on their transient characteristics, pressure peaks are either slow or fast, with separate charts provided for each type. A practical application involving a water pipeline with an irregular profile demonstrates the model’s effectiveness, showing strong agreement between calculated and chart-predicted (proposed methodology) values. This research provides water utilities with the ability to select the appropriate pipe’s resistance class required for water distribution systems by calculating the pressure peak value that may occur during filling procedures.

Modelling of the gas purification system for flue gas acid production

AbstractEstablishing a gas pressure model of the purification system to accurately estimate the system output pressure is a powerful guarantee to ensure the stable and efficient operation of the acid production process. This paper presents a study on the establishment of gas pressure models for three facilities in the purification system at the Guixi Smelter in China, including a pulse jet fabric filter, an electrostatic precipitator, and a drying tower. Through analysis of the operating mechanisms of these facilities, pressure mechanism models are established for the gas flows. Considering the nonlinearity and time‐varying nature of the model, an improved extended Kalman filter (EKF) algorithm is proposed to perform online identification of the unknown parameters within the model. Compared to the first‐order EKF, the improved algorithm achieves significantly better performance without incurring additional computational overhead. Field experiments on pressure estimation at the acid production site validate the reasonableness of the established models, as well as the efficacy of the proposed identification algorithm.

Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogen-powered internal combustion engines represent a promising alternative, offering the potential to reduce carbon-based emissions while improving efficiency. However, the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. Additionally, the artificial neural network is employed to predict the equivalent flame radius, thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines, due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions, across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy, achieving an RMSE consistently below 0.44% across various conditions. In comparison, GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally, the neural network effectively estimates the equivalent flame radius, maintaining an RMSE of less than 3%, ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore, this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis, offering a practical advantage in real-time diagnostics.

A novel numerical approach to elucidate experimental scatter in portal pressure measurement using ultrasound contrast agent.

The use of ultrasound contrast agents (UCAs) for estimating portal pressure has recently gained attention due to its clinical promise, yet variability in acoustic amplitude poses challenges. UCAs contain microbubbles (1-10 µm in diameter), and understanding their acoustic response is essential to address this variability. However, systematic exploration of factors influencing microbubble behavior remains limited in current literature. This paper introduces a novel finite element analysis-based framework for portal pressure estimation, bridging key gaps. Developed in two stages, the model first captures the subharmonic response of a single bubble to an acoustic excitation of 50 kPa at 4 MHz, highlighting the influence of bubble size on resonance frequency. In the second stage, single-bubble responses are extended to analyze how microbubble population, size, and spatial distribution affect portal pressure estimation. For the first time, this study elucidates the experimental scatter in pressure measurements through a comprehensive consideration of these variables, offering new directions for UCA-based clinical pressure estimation in applications such as portal and cardiac pressure assessment.

1D Network computational fluid dynamics for evaluating regional pressures in subjects with cement dust exposure.

Cement dust is a primary contributor to air pollution and is responsible for causing numerous respiratory diseases. The impact of cement dust exposure on the respiratory health of residents is increasing owing to the demand for construction associated with urbanization. Long-term inhalation of cement dust leads to a reduction in lung function, alterations in airway structure, increased inhalation and exhalation resistance, and heightened work of breath. In this study, we investigated the effects of cement dust exposure on lung function based on the pulmonary function test (PFT) and one-dimensional computational fluid dynamics (1D CFD). Statistical tests were performed to address the disparity of airway function between healthy and cement dust-exposed participants. The percent predicted values of forced vital capacity percent (FVC%) and forced expiratory volume in 1s (FEV1%) were found to be decreased in the group of dust-exposed participants. An elevation of regional pressure variation was found in cement dust-exposed airways during both inhalation and exhalation that was associated with alternations of airway structural features therein. The 1D CFD model is beneficial for a cost-effective estimation of airway regional pressure and provides valuable insights for more precise diagnosis and treatment planning in individuals exposed to cement dust.

The Estimation of Pressure of Blood Using ECG Image based on Artificial Intelligent Strategy

The Estimation of Pressure of Blood Using ECG Image based on Artificial Intelligent Strategy

Advection-based multiframe iterative correction for pressure estimation from velocity fields

Advection-based multiframe iterative correction for pressure estimation from velocity fields

Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

The progression of deep learning and the widespread adoption of sensors have facilitated automatic multi-view fusion (MVF) about the cardiovascular system (CVS) signals. However, prevalent MVF model architecture often amalgamates CVS signals from the same temporal step but different views into a unified representation, disregarding the asynchronous nature of cardiovascular events and the inherent heterogeneity across views, leading to catastrophic view confusion. Efficient training strategies specifically tailored for MVF models to attain comprehensive representations need simultaneous consideration. Crucially, real-world data frequently arrives with incomplete views, an aspect rarely noticed by researchers. Thus, the View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE) are specifically designed to emphasize the centrality of each view and harness unlabeled data to achieve superior fused representations. Additionally, we systematically define the missing-view problem for the first time and introduce prompt techniques to aid pretrained MVF models in flexibly adapting to various missing-view scenarios. Rigorous experiments involving atrial fibrillation detection, blood pressure estimation, and sleep staging—typical health monitoring tasks—demonstrate the remarkable advantage of our method in MVF compared to prevailing methodologies. Notably, the prompt technique requires finetuning less than 3% of the entire model's data, substantially fortifying the model's resilience to view missing while circumventing the need for complete retraining. The results demonstrate the effectiveness of our approaches, highlighting their potential for practical applications in cardiovascular health monitoring. Codes and models are released at URL.

Beyond the Average: Computation of Vertical Profiles in Dilute Pyroclastic Density Currents and Their Use in Shallow‐Water Models

AbstractPyroclastic density currents (PDCs) present significant hazards due to their high temperatures and dynamic pressures. Accurate estimation of dynamic pressure, vital for assessing potential damage, requires knowledge of the vertical variations of velocity and particle concentration within the PDC, particularly in the first few meters of the flow above the ground. Existing approaches to dynamic pressure calculations used in hazard assessment are often based on average values for velocity and particle volume fraction. These average values may misrepresent the flow dynamics, especially near the base of the flow where the gradients of flow variables are larger. Here, we present a new, physically based approach that allows for the calculation of the vertical profiles of velocity and concentration from a combination of depth‐averaged values for these properties and non‐dimensional flow parameters. Finally, we demonstrate the use of these profiles within an existing shallow‐water model and show its potential applications toward probabilistic hazard assessment.

Noninvasive estimation of central blood pressure through fluid-structure interaction modeling.

Central blood pressure (cBP) is considered a superior indicator of cardiovascular fitness than brachial blood pressure (bBP). Even though bBP is easy to measure noninvasively, it is usually higher than cBP due to pulse wave amplification, characterized by the gradual increase in peak systolic pressure during pulse wave propagation. In this study, we aim to develop an individualized transfer function that can accurately estimate cBP from bBP. We first construct a three-dimensional, patient-specific model of the upper limb arterial system using fluid-structure interaction simulations, incorporating variable material properties and complex boundary conditions. Then, we develop an analytical brachial-aortic transfer function based on novel solutions for compliant vessels. The accuracy of this transfer function is successfully validated against numerical simulation results, which effectively reproduce pulse wave propagation and amplification, with key hemodynamic parameters falling within the range of clinical measurements. Further analysis of the transfer function reveals that cBP is a linear combination of bBP and aortic flow rate in the frequency domain, with the coefficients determined by vessel geometry, material properties, and boundary conditions. Additionally, bBP primarily contributes to the steady component of cBP, while the aortic flow rate is responsible for the pulsatile component. Furthermore, local sensitivity analysis indicates that the lumen radius is the most influential parameter in accurately estimating cBP. Although not directly applicable clinically, the proposed transfer function enhances understanding of the underlying physics-highlighting the importance of aortic flow and lumen radius-and can guide the development of more practical transfer functions.

From BpTRU to OMRON: The impact of changing automated blood pressure measurement devices on blood pressure estimates among children and youth.

After six cycles of using the BpTRU (BT) BPM-300 blood pressure (BP) monitor, the Canadian Health Measures Survey (CHMS) introduced the OMRON (OM) IntelliSense HEM-907XL BP monitor in Cycle 7. This study assesses differences between BP values measured by both devices in children and youth aged 6 to 17 years and whether equations could be developed to compare BP measurements taken using the two devices. In Cycle 6 (2018 to 2019) of the CHMS, BP was measured using BT and OM devices. Between-device estimates of systolic BP (SBP), diastolic BP (DBP), and BP categories were compared for 233 children and youth aged 6 to 17 years. Sex, age, body mass index (BMI), and central obesity categories were examined in linear regression models to predict SBP and DBP values measured with OM based on those measured with BT. In the study sample, average SBP measured with OM was 9 millimetres of mercury (mmHg) higher than average SBP measured with BT, and average DBP measured with OM was 3 mmHg lower than DBP measured with BT. Normal BP prevalence based on OM measurements was 2.1 to 6.4 percentage points lower than the prevalence based on BT measurements, depending on which pediatric BP guidelines were applied. Between-device BP differences varied in magnitude by sex, age group, and BMI categories. Prediction equations developed using linear regression could not adequately account for the measurement differences between the two devices. Switching to the OM device in Cycle 7 will substantively affect pediatric BP estimates, preventing comparability with BP data from previous cycles. The impact of changing the BP devices on BP measurement should be acknowledged when reporting estimates of pediatric BP based on the CHMS.

Continuous Estimation of Blood Pressure by Utilizing Seismocardiogram Signal Features in Relation to Electrocardiogram.

There is an ongoing search for a reliable and continuous method of noninvasive blood pressure (BP) tracking. In this study, we investigate the feasibility of utilizing seismocardiogram (SCG) signals, i.e., chest motion caused by cardiac activity, for this purpose. This research is novel in examining the temporal relationship between the SCG-measured isovolumic moment and the electrocardiogram (PEPIM). Additionally, we compare these results with the traditionally measured pre-ejection period with the aortic opening marked as an endpoint (PEPAO). The accuracy of the BP estimation was evaluated beat to beat against invasively measured arterial BP. Data were collected on separate days as eighteen sets from nine subjects undergoing a medical procedure with anesthesia. Results for PEPIM showed a correlation of 0.67 ± 0.18 (p < 0.001), 0.66 ± 0.17 (p < 0.001), and 0.67 ± 0.17 (p < 0.001) when compared to systolic BP, diastolic BP, and mean arterial pressure (MAP), respectively. Corresponding results for PEPAO were equal to 0.61 ± 0.22 (p < 0.001), 0.61 ± 0.21 (p < 0.001), and 0.62 ± 0.22 (p < 0.001). Values of PEPIM were used to estimate MAP using two first-degree models, the linear regression model (achieved RMSE of 11.7 ± 4.0 mmHg) and extended model with HR (RMSE of 10.8 ± 4.2 mmHg), and two corresponding second-degree models (RMSE of 10.8 ± 3.7 mmHg and RMSE of 8.5 ± 3.4 mmHg for second-degree polynomial and second-degree extended, respectively). In the intrasubject testing of the second-degree model extended with HR based on PEPIM values, the mean error of MAP estimation in three follow-up measurements was in the range of 7.5 to 10.5 mmHg, without recalibration. This study demonstrates the method's potential for further research, particularly given that both proximal and distal pulses are measured in close proximity to the heart and cardiac output. This positioning may enhance the method's capacity to more accurately reflect central blood pressure compared to peripheral measurements.