Association of simultaneously measured four-limb blood pressures with cardiovascular function: a cross-sectional study

Song, Xiaohong; Li, Gaoyang; Qiao, Aike; Chen, Zhihui

doi:10.1186/s12938-016-0266-y

Cited by 13 publications

(11 citation statements)

References 19 publications

(26 reference statements)

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“…The original pulse wave data were from the “Study on Evaluation Method of Cardiovascular System Based on Non-invasive Detection of Blood Pressure and Pulse-Wave of Limbs 29 ”, which recruited 412 subjects and determined their physiological parameters and more than 12,000 cycles of pulse waves. The pulse and blood pressure signal measuring device was Fukuda VS-1500A.…”

Section: Methodsmentioning

confidence: 99%

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Watanabe

Anzai

et al. 2019

Sci Rep

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Owing to the diversity of pulse-wave morphology, pulse-based diagnosis is difficult, especially pulse-wave-pattern classification (PWPC). A powerful method for PWPC is a convolutional neural network (CNN). It outperforms conventional methods in pattern classification due to extracting informative abstraction and features. For previous PWPC criteria, the relationship between pulse and disease types is not clear. In order to improve the clinical practicability, there is a need for a CNN model to find the one-to-one correspondence between pulse pattern and disease categories. In this study, five cardiovascular diseases (CVD) and complications were extracted from medical records as classification criteria to build pulse data set 1. Four physiological parameters closely related to the selected diseases were also extracted as classification criteria to build data set 2. An optimized CNN model with stronger feature extraction capability for pulse signals was proposed, which achieved PWPC with 95% accuracy in data set 1 and 89% accuracy in data set 2. It demonstrated that pulse waves are the result of multiple physiological parameters. There are limitations when using a single physiological parameter to characterise the overall pulse pattern. The proposed CNN model can achieve high accuracy of PWPC while using CVD and complication categories as classification criteria.

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Section: Methodsmentioning

confidence: 99%

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Watanabe

Anzai

et al. 2019

Sci Rep

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“…The study, its experimental protocols, and relevant details were approved by the Institutional Ethics Committee of Beijing University of Technology. This study was a retrospective analysis of the “Study on Evaluation Method of Cardiovascular Disease Based on Non-invasive Detection of Blood Pressure and Pulse-Wave of Limbs” ( Song et al, 2016 ), which recruited 412 subjects, where the clinic data collection as shown in Figure 1A . Herein, the Fukuda VS-1500A, manufactured by Beijing Fukuda Electronic Medical Instrument Co., Ltd, obtained the pulse waveform of four limbs, synchronously measures the blood pressure of four limbs, and automatically calculates the pulse wave velocity, ankle-brachial index, cardio-ankle vascular index, etc.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, this presents a large gap in treating CVDs. Studies have shown that cardiovascular functions are characterized by pulse waves changes in hemodynamic diagnosis indexes (HDIs) are reflected by changes in pulse waveforms, which can be used for the early detection of CVDs symptoms (Chow et al, 2008;O'Rourke, 2009;Celermajer et al, 2012;SONG and QIAO, 2015;Wang et al, 2022). HDIs can be used to comprehensively evaluate the health status of a patient's CVS, particularly for patients older than 50 years and prone to CVDs, more accurately and directly in terms of total peripheral resistance (TPR), stroke volume (SV), arterial compliance (AC), etc., (Thenappan et al, 2015;Suzuki et al, 2019;Trammel and Sapra, 2020).…”

Section: Introductionmentioning

confidence: 99%

Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs

Song

Liu²,

Wang³

et al. 2023

Front. Bioeng. Biotechnol.

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Introduction: Hemodynamic diagnosis indexes (HDIs) can comprehensively evaluate the health status of the cardiovascular system (CVS), particularly for people older than 50 years and prone to cardiovascular disease (CVDs). However, the accuracy of non-invasive detection remains unsatisfactory. We propose a non-invasive HDIs model based on the non-linear pulse wave theory (NonPWT) applied to four limbs.Methods: This algorithm establishes mathematical models, including pulse wave velocity and pressure information of the brachial and ankle arteries, pressure gradient, and blood flow. Blood flow is key to calculating HDIs. Herein, we derive blood flow equation for different times of the cardiac cycle considering the four different distributions of blood pressure and pulse wave of four limbs, then obtain the average blood flow in a cardiac cycle, and finally calculate the HDIs.Results: The results of the blood flow calculations reveal that the average blood flow in the upper extremity arteries is 10.78 ml/s (clinically: 2.5–12.67 ml/s), and the blood flow in the lower extremity arteries is higher than that in the upper extremity. To verify model accuracy, the consistency between the clinical and calculated values is verified with no statistically significant differences (p < 0.05). Model IV or higher-order fitting is the closest. To verify the model generalizability, considering the risk factors of cardiovascular diseases, the HDIs are recalculated using model IV, and thus, consistency is verified (p < 0.05 and Bland-Altman plot).Conclusion: We conclude our proposed algorithmic model based on NonPWT can facilitate the non-invasive hemodynamic diagnosis with simpler operational procedures and reduced medical costs.

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“…This estimating method has been described in our previous study (Li et al, 2020). The brachial artery pressure was collected using a Fukuda pulse wave meter (Song et al, 2016), whereas the CO data were monitored using a color Doppler ultrasound. We performed numerical simulations of the different…”

Section: Verification By Clinical Experimentsmentioning

confidence: 99%

A Numerical Model for Simulating the Hemodynamic Effects of Enhanced External Counterpulsation on Coronary Arteries

Liu

et al. 2021

Front. Physiol.

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Traditional enhanced external counterpulsation (EECP) used for the clinical treatment of patients with coronary heart disease only assesses diastolic/systolic blood pressure (Q = D/S > 1.2). However, improvement of the hemodynamic environment surrounding vascular endothelial cells of coronary arteries after long-term application of EECP is the basis of the treatment. Currently, the quantitative hemodynamic mechanism is not well understood. In this study, a standard 0D/3D geometric multi-scale model of the coronary artery was established to simulate the hemodynamic effects of different counterpulsation modes on the vascular endothelium. In this model, the neural regulation caused by counterpulsation was thoroughly considered. Two clinical trials were carried out to verify the numerical calculation model. The results demonstrated that the increase in counterpulsation pressure amplitude and pressurization duration increased coronary blood perfusion and wall shear stress (WSS) and reduced the oscillatory shear index (OSI) of the vascular wall. However, the impact of pressurization duration was the predominant factor. The results of the standard model and the two real individual models indicated that a long pressurization duration would cause more hemodynamic risk areas by resulting in excessive WSS, which could not be reflected by the change in the Q value. Therefore, long-term pressurization during each cardiac cycle therapy is not recommended for patients with coronary heart disease and clinical treatment should not just pay attention to the change in the Q value. Additional physiological indicators can be used to evaluate the effects of counterpulsation treatment.

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Association of simultaneously measured four-limb blood pressures with cardiovascular function: a cross-sectional study

Cited by 13 publications

References 19 publications

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Pulse-Wave-Pattern Classification with a Convolutional Neural Network

Non-invasive hemodynamic diagnosis based on non-linear pulse wave theory applied to four limbs

A Numerical Model for Simulating the Hemodynamic Effects of Enhanced External Counterpulsation on Coronary Arteries

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