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1.
Front Physiol ; 14: 1100570, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36935738

RESUMO

Carotid-to-femoral pulse wave velocity (cf-PWV) is considered a critical index to evaluate arterial stiffness. For this reason, estimating Carotid-to-femoral pulse wave velocity (cf-PWV) is essential for diagnosing and analyzing different cardiovascular diseases. Despite its broader adoption in the clinical routine, the measurement process of carotid-to-femoral pulse wave velocity is considered a demanding task for clinicians and patients making it prone to inaccuracies and errors in the estimation. A smart non-invasive, and peripheral measurement of carotid-to-femoral pulse wave velocity could overcome the challenges of the classical assessment process and improve the quality of patient care. This paper proposes a novel methodology for the carotid-to-femoral pulse wave velocity estimation based on the use of the spectrogram representation from single non-invasive peripheral pulse wave signals [photoplethysmography (PPG) or blood pressure (BP)]. This methodology was tested using three feature extraction methods based on the semi-classical signal analysis (SCSA) method, the Law's mask for texture energy extraction, and the central statistical moments. Finally, each feature method was fed into different machine learning models for the carotid-to-femoral pulse wave velocity estimation. The proposed methodology obtained an $R2\geq0.90$ for all the peripheral signals for the noise-free case using the MLP model, and for the different noise levels added to the original signal, the SCSA-based features with the MLP model presented an $R2\geq0.91$ for all the peripheral signals at the level of noise. These results provide evidence of the capacity of spectrogram representation for efficiently assessing the carotid-to-femoral pulse wave velocity estimation using different feature methods. Future work will be done toward testing the proposed methodology for in-vivo signals.

2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 143-147, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-36085988

RESUMO

In this paper, a multiple linear regression model for estimating the Carotid-to-femoral pulse wave velocity (cf-PWV) from a single non-invasive peripheral pulse wave, namely blood pressure or photoplethysmography, is proposed. The training and testing datasets were extracted from in-silico, publicly available, pulse waves and hemodynamics data. The proposed model relies on a preprocessing and features extraction steps, which are performed using a semi-classical signal analysis (SCSA) method. The obtained results provide more evidence for the feasibility of machine learning and the SCSA method as a smart tool for the efficient assessment of the cf-PWV.


Assuntos
Artérias Carótidas , Análise de Onda de Pulso , Velocidade do Fluxo Sanguíneo , Artérias Carótidas/fisiologia , Artéria Femoral/fisiologia , Modelos Lineares , Análise de Onda de Pulso/métodos
3.
Front Physiol ; 13: 838593, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35392372

RESUMO

The blood flow dynamics in human arteries with hypertension disease is modeled using fractional calculus. The mathematical model is constructed using five-element lumped parameter arterial Windkessel representation. Fractional-order capacitors are used to represent the elastic properties of both proximal large arteries and distal small arteries measured from the heart aortic root. The proposed fractional model offers high flexibility in characterizing the arterial complex tree network. The results illustrate the validity of the new model and the physiological interpretability of the fractional differentiation order through a set of validation using human hypertensive patients. In addition, the results show that the fractional-order modeling approach yield a great potential to improve the understanding of the structural and functional changes in the large and small arteries due to hypertension disease.

4.
IEEE Open J Eng Med Biol ; 3: 69-77, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36860497

RESUMO

Goal: Modeling neurovascular coupling is very important to understand brain functions, yet challenging due to the complexity of the involved phenomena. An alternative approach was recently proposed where the framework of fractional-order modeling is employed to characterize the complex phenomena underlying the neurovascular. Due to its nonlocal property, a fractional derivative is suitable for modeling delayed and power-law phenomena. Methods: In this study, we analyze and validate a fractional-order model, which characterizes the neurovascular coupling mechanism. To show the added value of the fractional-order parameters of the proposed model, we perform a parameter sensitivity analysis of the fractional model compared to its integer counterpart. Moreover, the model was validated using neural activity-CBF data related to both event and block design experiments that were acquired using electrophysiology and laser Doppler flowmetry recordings, respectively. Results: The validation results show the aptitude and flexibility of the fractional-order paradigm in fitting a more comprehensive range of well-shaped CBF response behaviors while maintaining a low model complexity. Comparison with the standard integer-order models shows the added value of the fractional-order parameters in capturing various key determinants of the cerebral hemody-namic response, e.g., post-stimulus undershoot. This investigation authenticates the ability and adaptability of the fractional-order framework to characterize a wider range of well-shaped cerebral blood flow responses while preserving low model complexity through a series of unconstrained and constrained optimizations. Conclusions: The analysis of the proposed fractional-order model demonstrates that the proposed framework yields a powerful tool for a flexible characterization of the neurovascular coupling mechanism.

5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 5512-5517, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34892373

RESUMO

Central blood pressure is a vital signal that provides relevant physiological information about cardiovascular diseases risk factors. The standard clinical protocols for measuring these signals are challenging due to their invasive nature. This makes the estimation-based methods more convenient, however, they are usually not accurate as they fail to capture some important features of the central pressure waveforms. In this paper, we propose a novel data-driven approach that combines machine learning tools and cross-relation-based blind estimation methods to reconstruct the aortic blood pressure waves from the distorted peripheral pressure signals. Due to the lack of large real datasets, in this study, we utilize virtual pulse waves in-silico databases to train the machine learning models. The performance of the proposed approach is compared with the pure machine learning-based model and the cross-relation-based blind estimation approach. In both cases, the hybrid approach shows promising results as the root-mean-squared error has been reduced by 25% with regards to the pure machine learning method and by 40% compared to the cross-relation approach.


Assuntos
Algoritmos , Pressão Arterial , Pressão Sanguínea , Determinação da Pressão Arterial , Aprendizado de Máquina
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 5559-5565, 2021 11.
Artigo em Inglês | MEDLINE | ID: mdl-34892384

RESUMO

Arterial compliance is a vital determinant of the ventriculo-arterial coupling dynamic. Its variation is detrimental to cardiovascular functions and associated with heart diseases. Accordingly, assessment and measurement of arterial compliance are essential in the diagnosis and treatment of chronic arterial insufficiency. Recently, experimental and theoretical studies have recognized the power of fractional calculus to perceive viscoelastic blood vessel structure and biomechanical properties. This paper presents five fractional-order model representations to describe the dynamic relationship between the aortic blood pressure input and blood volume. Each configuration incorporates a fractional-order capacitor element (FOC) to lump the apparent arterial compliance's complex and frequency dependence properties. FOC combines both resistive and capacitive attributes within a unified component, which can be controlled through the fractional differentiation order factor, α. Besides, the equivalent capacitance of FOC is by its very nature frequency-dependent, compassing the complex properties using only a few numbers of parameters. The proposed representations have been compared with generalized integer-order models of arterial compliance. Both models have been applied and validated using different aortic pressure and flow rate data acquired from various species such as humans, pigs, and dogs. The results have shown that the fractional-order framework is able to accurately reconstruct the dynamic of the complex and frequency-dependent apparent compliance dynamic and reduce the complexity. It seems that this new paradigm confers a prominent potential to be adopted in clinical practice and basic cardiovascular mechanics research.


Assuntos
Artérias , Hemodinâmica , Animais , Complacência (Medida de Distensibilidade) , Humanos
7.
Physiol Meas ; 42(4)2021 05 13.
Artigo em Inglês | MEDLINE | ID: mdl-33761470

RESUMO

Objective. Recent studies have demonstrated the advantages of fractional-order calculus tools for probing the viscoelastic properties of collagenous tissue, characterizing the arterial blood flow and red cell membrane mechanics, and modeling the aortic valve cusp. In this article, we present novel lumped-parameter equivalent circuit models for apparent arterial compliance using a fractional-order capacitor (FOC). FOCs, which generalize capacitors and resistors, display a fractional-order behavior that can capture both elastic and viscous properties through a power-law formulation.Approach. The proposed framework describes the dynamic relationship between the blood-pressure input and the blood volume, using linear fractional-order differential equations.Main results. The results show that the proposed models present a reasonable fit with thein-silicodata of more than 4000 subjects. Additionally, strong correlations have been identified between the fractional-order parameter estimates and the central hemodynamic determinants as well as the pulse-wave velocity indexes.Significance. Therefore, the fractional-order-based paradigm for arterial compliance shows notable potential as an alternative tool in the analysis of arterial stiffness.


Assuntos
Rigidez Vascular , Artérias , Pressão Sanguínea , Complacência (Medida de Distensibilidade) , Hemodinâmica , Humanos , Análise de Onda de Pulso
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2723-2727, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018569

RESUMO

Central aortic blood pressure (CABP) is a very-well recognized source of information to asses the cardiovascular system conditions. However, the clinical measurement protocol of this pulse wave is very intrusive and burdensome as it requires expert staff and complicated invasive settings. On the other hand, the measurement of peripheral blood pressure is much more straightforward and easy-to-get non-invasively. Several mathematical tools have been employed in the past few decades to reconstruct CABP waveforms from distorted peripheral pressure signals. More specifically, the cross-relation approach together with the widely used least-squares method, are shown to be effective as a way to estimate CABP waves. In this paper, we propose an improved cross-relation method that leverages the values of the diastolic and systolic pressures as box constraints. In addition, a mean-matching criterion is introduced to relax the need for the input and output mean values to be strictly equal. Using the proposed method, the root mean squared error is reduced by approximately 20% while the computational complexity is not significantly increased.


Assuntos
Aorta , Determinação da Pressão Arterial , Pressão Sanguínea , Humanos , Análise dos Mínimos Quadrados , Reprodutibilidade dos Testes
9.
IEEE Open J Eng Med Biol ; 1: 249-256, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-35402939

RESUMO

Goal: Coronavirus disease (COVID-19) is a contagious disease caused by a newly discovered coronavirus, initially identified in the mainland of China, late December 2019. COVID-19 has been confirmed as a higher infectious disease that can spread quickly in a community population depending on the number of susceptible and infected cases and also depending on their movement in the community. Since January 2020, COVID-19 has reached out to many countries worldwide, and the number of daily cases remains to increase rapidly. Method: Several mathematical and statistical models have been developed to understand, track, and forecast the trend of the virus spread. Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model is one of the most promising epidemiological models that has been suggested for estimating the transmissibility of the COVID-19. In the present study, we propose a fractional-order SEIQRDP model to analyze the COVID-19 pandemic. In the recent decade, it has proven that many aspects in many domains can be described very successfully using fractional order differential equations. Accordingly, the Fractional-order paradigm offers a flexible, appropriate, and reliable framework for pandemic growth characterization. In fact, due to its non-locality properties, a fractional-order operator takes into consideration the variables' memory effect, and hence, it takes into account the sub-diffusion process of confirmed and recovered cases. Results-The validation of the studied fractional-order model using real COVID-19 data for different regions in China, Italy, and France show the potential of the proposed paradigm in predicting and understanding the pandemic dynamic. Conclusions: Fractional-order epidemiological models might play an important role in understanding and predicting the spread of the COVID-19, also providing relevant guidelines for controlling the pandemic.

10.
IEEE Open J Eng Med Biol ; 1: 123-132, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-35402942

RESUMO

Goal: Fractional-order Windkessel model is proposed to describe the aortic input impedance. Compared with the conventional arterial Windkessel, the main advantage of the proposed model is the consideration of the viscoelastic nature of the arterial wall using the fractional-order capacitor (FOC). Methods: The proposed model, along with the standard two-element Windkessel, three-element Windkessel, and the viscoelastic Windkessel models, are assessed and compared using in-silico data. Results: The results show that the fractional-order model fits better the moduli of the aortic input impedance and fairly approximates the phase angle. In addition, by its very nature, the pseudo-capacitance of FOC makes the proposed model's dynamic compliance complex and frequency-dependent. Conclusions: The analysis of the proposed fractional-order model indicates that fractional-order impedance yields a powerful tool for a flexible characterization of the arterial hemodynamics.

11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2019: 5018-5023, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31946987

RESUMO

Arterial system is completely coupled with the heart, such that the contractile state of the left ventricle and its produced central blood pressure (the pressure in the aorta) are in tune with the arterial mechanical properties. This study investigates the use of fractional-order capacitor and resistor elements to expose, and estimate the main arterial mechanical properties. We propose a simple two-element fractional-order Windkessel model that is able to capture the real aortic impedance dynamic for different cardiac physiological states. To perform a quantitative validation, in-silico ascending aortic blood pressure and flow database of 3,325 virtual subjects was used. The proposed model provides new simplified tool for "hemodynamic problem" solving, offering a pioneer way for a better understanding of vascular mechanical properties dependency on hemodynamic changes such as arterial viscoelasticity.


Assuntos
Aorta , Impedância Elétrica , Hemodinâmica , Modelos Cardiovasculares , Interface Usuário-Computador , Pressão Sanguínea , Determinação da Pressão Arterial , Humanos , Resistência Vascular
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2018: 5261-5266, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30441525

RESUMO

Arterial hemodynamic assessment has always been essential for clinical Cardiovascular System (CVS) diagnosis. Using Windkessel (WK) lumped parametric model as non-invasive measurement tool provides the potential of achieving a very convenient, computational inexpensive and accurate prediction of the arterial parameters. Many versions of WK models have been proposed and extensively studied, over the last century. In general, they can be classified into two categories: elastic and viscoelastic models. Recently, several studies have discussed the potential of describing the arterial wall viscoelasticity using fractional order models, reducing the number of parameters and exposing a natural response. Hence, a key missing item in the arterial Windkessel modeling is a fractional-order analog component that can provide a reliable, realistic and reduced representation of the fractional viscoelasticity behavior. In this paper, we present, for the first time, a three-element fractional-order viscoelastic Windkessel model. The proposed model incorporates a fractional-order capacitor that substitutes the ideal capacitor of standard three-elements WK model. The latter non-ideal element combines both resistive and capacitive properties which displays viscoelastic behavior of the arterial vessel. The contribution of both properties is controlled by the fractional differentiation order α enabling an accurate and reliable physiological description.


Assuntos
Artérias , Hemodinâmica , Modelos Cardiovasculares , Viscosidade
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