ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans.

Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and -0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applying our method when handling liver segmentation with small and discontinuous liver regions, as well as blurred liver boundaries. The code is available at the website: https://github.com/Jouiry/ResTransUNet.

Context-aware and local-aware fusion with transformer for medical image segmentation.

Objective. Convolutional neural networks (CNNs) have made significant progress in medical image segmentation tasks. However, for complex segmentation tasks, CNNs lack the ability to establish long-distance relationships, resulting in poor segmentation performance. The characteristics of intra-class diversity and inter-class similarity in images increase the difficulty of segmentation. Additionally, some focus areas exhibit a scattered distribution, making segmentation even more challenging.Approach. Therefore, this work proposed a new Transformer model, FTransConv, to address the issues of inter-class similarity, intra-class diversity, and scattered distribution in medical image segmentation tasks. To achieve this, three Transformer-CNN modules were designed to extract global and local information, and a full-scale squeeze-excitation module was proposed in the decoder using the idea of full-scale connections.Main results. Without any pre-training, this work verified the effectiveness of FTransConv on three public COVID-19 CT datasets and MoNuSeg. Experiments have shown that FTransConv, which has only 26.98M parameters, outperformed other state-of-the-art models, such as Swin-Unet, TransAttUnet, UCTransNet, LeViT-UNet, TransUNet, UTNet, and SAUNet++. This model achieved the best segmentation performance with a DSC of 83.22% in COVID-19 datasets and 79.47% in MoNuSeg.Significance. This work demonstrated that our method provides a promising solution for regions with high inter-class similarity, intra-class diversity and scatter distribution in image segmentation.

MSMCNet: Differential context drives accurate localization and edge smoothing of lesions for medical image segmentation.

Medical image segmentation plays a crucial role in clinical assistance for diagnosis. The UNet-based network architecture has achieved tremendous success in the field of medical image segmentation. However, most methods commonly employ element-wise addition or channel merging to fuse features, resulting in smaller differentiation of feature information and excessive redundancy. Consequently, this leads to issues such as inaccurate lesion localization and blurred boundaries in segmentation. To alleviate these problems, the Multi-scale Subtraction and Multi-key Context Conversion Networks (MSMCNet) are proposed for medical image segmentation. Through the construction of differentiated contextual representations, MSMCNet emphasizes vital information and achieves precise medical image segmentation by accurately localizing lesions and enhancing boundary perception. Specifically, the construction of differentiated contextual representations is accomplished through the proposed Multi-scale Non-crossover Subtraction (MSNS) module and Multi-key Context Conversion Module (MCCM). The MSNS module utilizes the context of MCCM coding and redistribute the value of feature map pixels. Extensive experiments were conducted on widely used public datasets, including the ISIC-2018 dataset, COVID-19-CT-Seg dataset, Kvasir dataset, as well as a privately constructed traumatic brain injury dataset. The experimental results demonstrated that our proposed MSMCNet outperforms state-of-the-art medical image segmentation methods across different evaluation metrics.

Reconstruction of central arterial pressure waveform based on CBi-SAN network from radial pressure waveform.

The central arterial pressure (CAP) is an important physiological indicator of the human cardiovascular system which represents one of the greatest threats to human health. Accurate non-invasive detection and reconstruction of CAP waveforms are crucial for the reliable treatment of cardiovascular system diseases. However, the traditional methods are reconstructed with relatively low accuracy, and some deep learning neural network models also have difficulty in extracting features, as a result, these methods have potential for further advancement. In this study, we proposed a novel model (CBi-SAN) to implement an end-to-end relationship from radial artery pressure (RAP) waveform to CAP waveform, which consisted of the convolutional neural network (CNN), the bidirectional long-short-time memory network (BiLSTM), and the self-attention mechanism to improve the performance of CAP reconstruction. The data on invasive measurements of CAP and RAP waveform were used in 62 patients before and after medication to develop and validate the performance of CBi-SAN model for reconstructing CAP waveform. We compared it with traditional methods and deep learning models in mean absolute error (MAE), root mean square error (RMSE), and Spearman correlation coefficient (SCC). Study results indicated the CBi-SAN model performed great performance on CAP waveform reconstruction (MAE: 2.23 ± 0.11 mmHg, RMSE: 2.21 ± 0.07 mmHg), concurrently, the best reconstruction effect was obtained in the central artery systolic pressure (CASP) and the central artery diastolic pressure(CADP) (RMSECASP: 2.94 ± 0.48 mmHg, RMSECADP: 1.96 ± 0.06 mmHg). These results implied the performance of the CAP reconstruction based on CBi-SAN model was superior to the existing methods, hopped to be effectively applied to clinical practice in the future.

Deep learning-based lung image registration: A review.

Lung image registration can effectively describe the relative motion of lung tissues, thereby helping to solve series problems in clinical applications. Since the lungs are soft and fairly passive organs, they are influenced by respiration and heartbeat, resulting in discontinuity of lung motion and large deformation of anatomic features. This poses great challenges for accurate registration of lung image and its applications. The recent application of deep learning (DL) methods in the field of medical image registration has brought promising results. However, a versatile registration framework has not yet emerged due to diverse challenges of registration for different regions of interest (ROI). DL-based image registration methods used for other ROI cannot achieve satisfactory results in lungs. In addition, there are few review articles available on DL-based lung image registration. In this review, the development of conventional methods for lung image registration is briefly described and a more comprehensive survey of DL-based methods for lung image registration is illustrated. The DL-based methods are classified according to different supervision types, including fully-supervised, weakly-supervised and unsupervised. The contributions of researchers in addressing various challenges are described, as well as the limitations of these approaches. This review also presents a comprehensive statistical analysis of the cited papers in terms of evaluation metrics and loss functions. In addition, publicly available datasets for lung image registration are also summarized. Finally, the remaining challenges and potential trends in DL-based lung image registration are discussed.

[Corona virus disease 2019 lesion segmentation network based on an adaptive joint loss function].

Corona virus disease 2019 (COVID-19) is an acute respiratory infectious disease with strong contagiousness, strong variability, and long incubation period. The probability of misdiagnosis and missed diagnosis can be significantly decreased with the use of automatic segmentation of COVID-19 lesions based on computed tomography images, which helps doctors in rapid diagnosis and precise treatment. This paper introduced the level set generalized Dice loss function (LGDL) in conjunction with the level set segmentation method based on COVID-19 lesion segmentation network and proposed a dual-path COVID-19 lesion segmentation network (Dual-SAUNet++) to address the pain points such as the complex symptoms of COVID-19 and the blurred boundaries that are challenging to segment. LGDL is an adaptive weight joint loss obtained by combining the generalized Dice loss of the mask path and the mean square error of the level set path. On the test set, the model achieved Dice similarity coefficient of (87.81 ± 10.86)%, intersection over union of (79.20 ± 14.58)%, sensitivity of (94.18 ± 13.56)%, specificity of (99.83 ± 0.43)% and Hausdorff distance of 18.29 ± 31.48 mm. Studies indicated that Dual-SAUNet++ has a great anti-noise capability and it can segment multi-scale lesions while simultaneously focusing on their area and border information. The method proposed in this paper assists doctors in judging the severity of COVID-19 infection by accurately segmenting the lesion, and provides a reliable basis for subsequent clinical treatment.

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.

RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images.

Liver cancer is one of the leading causes of cancer-related deaths worldwide. Automatic liver and tumor segmentation are of great value in clinical practice as they can reduce surgeons' workload and increase the probability of success in surgery. Liver and tumor segmentation is a challenging task because of the different sizes, shapes, blurred boundaries of livers and lesions, and low-intensity contrast between organs within patients. To address the problem of fuzzy livers and small tumors, we propose a novel Residual Multi-scale Attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, i.e., Res-SE-Block and MAB. The Res-SE-Block can mitigate the problem of gradient disappearance by residual connection and enhance the quality of representations by explicitly modeling the interdependencies and feature recalibration between the channels of features. The MAB can exploit rich multi-scale feature information and capture inter-channel and inter-spatial relationships of features simultaneously. In addition, a hybrid loss function, that combines focal loss and dice loss, is designed to improve segmentation accuracy and speed up convergence. We evaluated the proposed method on two publicly available datasets, i.e., LiTS and 3D-IRCADb. Our proposed method achieved better performance than the other state-of-the-art methods, with dice scores of 0.9552 and 0.9697 for LiTS and 3D-IRCABb liver segmentation, and dice scores of 0.7616 and 0.8307 for LiTS and 3D-IRCABb liver tumor segmentation.

Extracting Multi-Scale and Salient Features by MSE Based U-Structure and CBAM for Sleep Staging.

According to the World Health Organization, more and more people in the world are suffering from somnipathy. Automatic sleep staging is critical for assessing sleep quality and assisting in the diagnosis of psychiatric and neurological disorders caused by somnipathy. Many researchers employ deep learning methods for sleep stage classification and have achieved high performance. However, there are still no effective methods to modeling intrinsic characteristics of salient wave in different sleep stages from physiological signals. And transition rules hidden in signals from one to another sleep stage cannot be identified and captured. In addition, class imbalance problem in dataset is not conducive to building a robust classification model. To solve these problems, we construct a deep neural network combining MSE(Multi-Scale Extraction) based U-structure and CBAM (Convolutional Block Attention Module) to extract the multi-scale salient waves from single-channel EEG signals. The U-structured convolutional network with MSE is utilized to extract multi-scale features from raw EEG signals. After that, the CBAM is used to focus more on salient variation and then learn transition rules between successive sleep stages. Further, a class adaptive weight cross entropy loss function is proposed to solve the class imbalance problem. Experiments in three public datasets show that our model greatly outperform the state-of-the-art results compared with existing methods. The overall accuracy and macro F1-score (Sleep-EDF-39: 90.3%-86.2, Sleep-EDF-153: 89.7%-85.2, SHHS: 86.8%-83.5) on three public datasets suggest that the proposed model is very promising to completely take place of human experts for sleep staging.

Estimation of cardiac stroke volume from radial pulse waveform by artificial neural network.

BACKGROUND AND OBJECTIVES: Stroke volume (SV) and cardiac output (CO) are the key indicators for the evaluation of cardiac function and hemodynamic status during the perioperative period, which are very important in the detection and treatment of cardiovascular diseases. Traditional CO and SV measurement methods have problems such as complex operation, low precision and poor generalization ability. METHODS: In this paper, a method for estimating stroke volume based on cascade artificial neural network (ANN) and time domain features of radial pulse waveform (SVANN) was proposed. The simulation datasets of 4000 radial pulse waveforms and stroke volume (SVmeas) were generated by a 55 segment transmission line model of the human systemic vasculature and a recursive algorithm. The ANN was trained and tested by 10-fold cross-validation, and compared with 12 traditional models. RESULTS: Experimental results showed that the Pearson correlation coefficients and mean difference between SVANN and SVmeas (R=0.95, mean standard deviation (SD) = 0.00 ± 6.45) were better than the best results of the 12 traditional models. Moreover, as increasing the number of training samples, the performance improvement of the ANN (R=0.94(Δ + 0.04), mean ± SD = 0.00 ± 6.38(Δ± 2.02)) was better than the other best model, namely, multiple linear regression model (MLR) (R=0.93(Δ + 0.03), mean ± SD = 0.00 ± 6.99(Δ± 1.50)). CONCLUSIONS: A method is proposed to estimate cardiac stroke volume by the ANN with time domain features of radial pulse wave. It avoids the complicated modeling process based on hemodynamics within traditional models, improves the estimation accuracy of SV, and has a good generalization ability.

[Research progress in lung parenchyma segmentation based on computed tomography].

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Estimation of wave reflection in aorta from radial pulse waveform by artificial neural network: a numerical study.

BACKGROUND AND OBJECTIVE: Wave reflection in aorta has been shown to have incremental value for predicting cardiovascular events. However, its estimation by wave separation analysis (WSA) is complex. METHODS: In this study, a novel method was proposed based on a cascade artificial neural network (ANN) for wave reflection estimation by the frequency features of radial pressure waveform alone. The simulation database of 4000 samples was generated by a 55-segment transmission line model of human arterial tree and was used for evaluating the ANN with 10-fold cross validation for the estimation of reflection magnitude (RMANN) and reflection index (RIANN) of wave reflection in aorta. RM and RI also were estimated by the WSA with a triangle waveform of aortic flow (RMWSA and RIWSA) and with a real aortic flow waveform (RMRef and RIRef) as reference values. RESULTS: The results showed the correlation coefficient and mean difference between RMANN and RMRef (R2 = 0.92, mean ±â€¯standard deviation (SD) = 0.0 ±â€¯0.02) and those between RIANN and RIRef (R2 = 0.91, mean ±â€¯SD = 0.0 ±â€¯0.01) were better than those between RMWSA and RMRef (R2 = 0.51, mean ±â€¯SD = 0.01 ±â€¯0.07) and those between RIWSA and RIRef (R2 = 0.50, mean ±â€¯SD = 0.0 ±â€¯0.02). As the sample diversity in the simulation database was increased but the total number of samples keeps constant, the advantage of the ANN, though decreased slightly, became more significant than those of WSA (RMANN VS. RMRef and RIANN VS. RIRef: R2 = 0.88 and 0.88, mean ±â€¯SD = 0.0 ±â€¯0.05 and 0.0 ±â€¯0.05; RMWSA VS. RMRef and RIWSA VS. RIRef: R2 = 0.24 and 0.24, mean ±â€¯SD = 0.07 ±â€¯0.24 and 0.02 ±â€¯0.08, respectively). In addition, the ANN can achieve better results than the traditional method WSA even only two hidden neurons are used. CONCLUSIONS: ANN is a potential method for the estimation of wave reflection in aorta by a single radial pulse waveform, but further validation of this method in clinic trials is needed.

A review on low-dimensional physics-based models of systemic arteries: application to estimation of central aortic pressure.

The physiological processes and mechanisms of an arterial system are complex and subtle. Physics-based models have been proven to be a very useful tool to simulate actual physiological behavior of the arteries. The current physics-based models include high-dimensional models (2D and 3D models) and low-dimensional models (0D, 1D and tube-load models). High-dimensional models can describe the local hemodynamic information of arteries in detail. With regard to an exact model of the whole arterial system, a high-dimensional model is computationally impracticable since the complex geometry, viscosity or elastic properties and complex vectorial output need to be provided. For low-dimensional models, the structure, centerline and viscosity or elastic properties only need to be provided. Therefore, low-dimensional modeling with lower computational costs might be a more applicable approach to represent hemodynamic properties of the entire arterial system and these three types of low-dimensional models have been extensively used in the study of cardiovascular dynamics. In recent decades, application of physics-based models to estimate central aortic pressure has attracted increasing interest. However, to our best knowledge, there has been few review paper about reconstruction of central aortic pressure using these physics-based models. In this paper, three types of low-dimensional physical models (0D, 1D and tube-load models) of systemic arteries are reviewed, the application of three types of models on estimation of central aortic pressure is taken as an example to discuss their advantages and disadvantages, and the proper choice of models for specific researches and applications are advised.

N-Point Moving Average: A Special Generalized Transfer Function Method for Estimation of Central Aortic Blood Pressure.

OBJECTIVE: N-point moving average (NPMA) is a simplified method of central aortic systolic pressure (CASP) estimation in comparison with the generalized transfer function (GTF). The fundamental difference or similarity between the methods is not established. This study investigates theoretical properties of NPMA relative to GTF and explores the integer and fractional denominator for the averaging process in the NPMA. METHODS: Convolution of a specified square wave and the radial (or brachial) blood pressure waveform constituted the NPMA . A single uniform tube model-based TF (MTF) was employed to investigate potential physiological meaning of NPMA. In experimental analysis, invasive, simultaneously recorded aortic and radial pressure waveforms were obtained in 62 subjects under control conditions and following nitroglycerin administration. CASP was estimated by NPMA (), GTF ( ), and MTF (CASP MTF) from radial waveforms by tenfold cross validation. RESULTS: Theoretical analysis showed that NPMA was an inversed constant TF. Its spectrum matched that of MTF in low frequency (<4 Hz for radial and <5 Hz for brachial) by optimizing reflection coefficient and propagation time. Experiment results showed the NPMA optimized fractional denominator of K = 4.4 significantly decreased the mean difference between CASPNPMA and measured CASP to 0.0 ± 4.7 mmHg from -1.8 ± 4.6 mmHg for integer denominator of K = 4. CASPNPMA correlated with CASPMTF and CASP GTF (r2 = 0.99 and 0.97, mean difference: -0.3 ± 1.8 and 0.5 ± 2.7 mmHg). CONCLUSION: This study demonstrated that NPMA is similar in nature to the GTF.

Effect of Heart Rate on Arterial Stiffness as Assessed by Pulse Wave Velocity.

Vascular assessment is becoming increasingly important in the diagnosis of cardiovascular diseases. In particular, clinical assessment of arterial stiffness, as measured by pulse wave velocity (PWV), is gaining increased interest due to the recognition of PWV as an influential factor on the prognosis of hypertension as well as being an independent predictor of cardiovascular and all-cause mortality. Whilst age and blood pressure are established as the two major determinants of PWV, the influence of heart rate on PWV measurements remains controversial with conflicting results being observed in both acute and epidemiological studies. In a majority of studies investigating the acute effects of heart rate on PWV, results were confounded by concomitant changes in blood pressure. Observations from epidemiological studies have also failed to converge, with approximately just half of such studies reporting a significant blood-pressure-independent association between heart rate and PWV. Further to the lack of consensus on the effects of heart rate on PWV, the possible mechanisms contributing to observed PWV changes with heart rate have yet to be fully elucidated, although many investigators have attributed heart-rate related changes in arterial stiffness to the viscoelasticity of the arterial wall. With elevated heart rate being an independent prognostic factor of cardiovascular disease and its association with hypertension, the interaction between heart rate and PWV continues to be relevant in assessing cardiovascular risk.

Mechanism underlying the heart rate dependency of wave reflection in the aorta: a numerical simulation.

Arterial wave reflection has been shown to have a significant dependence on heart rate (HR). However, the underlying mechanisms inherent in the HR dependency of wave reflection have not been well established. This study aimed to investigate the potential mechanisms and role of arterial viscoelasticity using a 55-segment transmission line model of the human arterial tree combined with a fractional viscoelastic model. At varying degrees of viscoelasticity modeled as fractional order parameter α, reflection magnitude (RM), reflection index (RI), augmentation index (AIx), and a proposed novel normalized reflection coefficient (Γnorm) were estimated at different HRs from 60 to 100 beats/min with a constant mean flow of 70 ml/s. RM, RI, AIx, and Γnorm at the ascending aorta decreased linearly with increasing HR at all degrees of viscoelasticity. The means ± SD of the HR dependencies of RM, RI, AIx, and Γnorm were -0.042 ± 0.004, -0.018 ± 0.001, -1.93 ± 0.55%, and -0.037 ± 0.002 per 10 beats/min, respectively. There was a significant and nonlinear reduction in RM, RI, and Γnorm with increasing α at all HRs. In addition, HR and α have a more pronounced effect on wave reflection at the aorta than at peripheral arteries. The potential mechanism of the HR dependency of wave reflection was explained by the inverse dependency of the reflection coefficient on frequency, with the harmonics of the pulse waveform moving toward higher frequencies with increasing HR. This HR dependency can be modulated by arterial viscoelasticity. NEW & NOTEWORTHY This in silico study addressed the underlying mechanisms of how heart rate influences arterial wave reflection based on a transmission line model and elucidated the role of arterial viscoelasticity in the dependency of arterial wave reflection on heart rate. This study provides insights into wave reflection as a frequency-dependent phenomenon and demonstrates the validity of using reflection magnitude and reflection index as wave reflection indexes.

Estimation of Pulse Transit Time From Radial Pressure Waveform Alone by Artificial Neural Network.

OBJECTIVE: To validate the feasibility of the estimation of pulse transit time (PTT) by artificial neural network (ANN) from radial pressure waveform alone. METHODS: A cascade ANN with ten-fold cross validation was applied to invasively and simultaneously recorded aortic and radial pressure waveforms during rest and nitroglycerin infusion () for the estimation of mean and beat-to-beat PTT. The results of the ANN models were compared to a multiple linear regression (LR) model when the features of radial arterial pressure waveform in time and frequency domains were used as the predictors of the models. RESULTS: For the estimation of mean PTT and beat-to-beat PTT by ANN ( ), the correlation coefficient between the and the measured PTT () (mean: ; beat-to-beat: ) is higher than that between the PTT estimated by LR ( ) and (mean: ; beat-to-beat: ). The standard deviation (SD) of the difference between the and ( ; beat-to-beat: ) is significantly less than that between the and (; beat-to-beat: 10 ms), but no significant difference exists between their mean ( ). The lack of frequency features of radial pressure waveform caused obvious reduction in the correlation coefficient and SD of the difference between the and . The performance of the ANN was improved by increasing the sample number but not by increasing the neuron number. CONCLUSION: ANN is a potential method of PTT estimation from a single pressure measurement at radial artery.

Effects of cardiac timing and peripheral resistance on measurement of pulse wave velocity for assessment of arterial stiffness.

To investigate the effects of heart rate (HR), left ventricular ejection time (LVET) and wave reflection on arterial stiffness as assessed by pulse wave velocity (PWV), a pulse wave propagation simulation system (PWPSim) based on the transmission line model of the arterial tree was developed and was applied to investigate pulse wave propagation. HR, LVET, arterial elastic modulus and peripheral resistance were increased from 60 to 100 beats per minute (bpm), 0.1 to 0.45 seconds, 0.5 to 1.5 times and 0.5 to 1.5 times of the normal value, respectively. Carotid-femoral PWV (cfPWV) and brachial-ankle PWV (baPWV) were calculated by intersecting tangent method (cfPWVtan and baPWVtan), maximum slope (cfPWVmax and baPWVmax), and using the Moens-Korteweg equation ([Formula: see text] and [Formula: see text]). Results showed cfPWV and baPWV increased significantly with arterial elastic modulus but did not increase with HR when using a constant elastic modulus. However there were significant LVET dependencies of cfPWVtan and baPWVtan (0.17 ± 0.13 and 0.17 ± 0.08 m/s per 50 ms), and low peripheral resistance dependencies of cfPWVtan, cfPWVmax, baPWVtan and baPWVmax (0.04 ± 0.01, 0.06 ± 0.04, 0.06 ± 0.03 and 0.09 ± 0.07 m/s per 10% peripheral resistance), respectively. This study demonstrated that LVET dominates the effect on calculated PWV compared to HR and peripheral resistance when arterial elastic modulus is constant.

Arterial viscoelasticity: role in the dependency of pulse wave velocity on heart rate in conduit arteries.

Experimental investigations have established that the stiffness of large arteries has a dependency on acute heart rate (HR) changes. However, the possible underlying mechanisms inherent in this HR dependency have not been well established. This study aimed to explore a plausible viscoelastic mechanism by which HR exerts an influence on arterial stiffness. A multisegment transmission line model of the human arterial tree incorporating fractional viscoelastic components in each segment was used to investigate the effect of varying fractional order parameter (α) of viscoelasticity on the dependence of aortic arch to femoral artery pulse wave velocity (afPWV) on HR. HR was varied from 60 to 100 beats/min at a fixed mean flow of 100 ml/s. PWV was calculated by intersecting tangent method (afPWVTan) and by phase velocity from the transfer function (afPWVTF) in the time and frequency domain, respectively. PWV was significantly and positively associated with HR for α ≥ 0.6; for α = 0.6, 0.8, and 1, HR-dependent changes in afPWVTan were 0.01 ± 0.02, 0.07 ± 0.04, and 0.22 ± 0.09 m/s per 5 beats/min; HR-dependent changes in afPWVTF were 0.02 ± 0.01, 0.12 ± 0.00, and 0.34 ± 0.01 m/s per 5 beats/min, respectively. This crosses the range of previous physiological studies where the dependence of PWV on HR was found to be between 0.08 and 0.10 m/s per 5 beats/min. Therefore, viscoelasticity of the arterial wall could contribute to mechanisms through which large artery stiffness changes with changing HR. Physiological studies are required to confirm this mechanism.NEW & NOTEWORTHY This study used a transmission line model to elucidate the role of arterial viscoelasticity in the dependency of pulse wave velocity on heart rate. The model uses fractional viscoelasticity concepts, which provided novel insights into arterial hemodynamics. This study also provides a means of assessing the clinical manifestation of the association of pulse wave velocity and heart rate.

Estimation of aortic systolic blood pressure from radial systolic and diastolic blood pressures alone using artificial neural networks.

BACKGROUND: Current aortic SBP estimation methods require recording of a peripheral pressure waveform, a step with no consensus on method. This study investigates the possibility of aortic SBP estimation from radial SBP and DBP using artificial neural networks (ANN) with [ANNSBP.DBP.heart rate (HR)] and without HR (ANNSBP.DBP). METHODS: Ten-fold cross validation was applied to invasive, simultaneously recorded aortic and radial pressure during rest and nitroglycerin infusion (n = 62 patients). The results of the ANN models were compared with an ANN model using additional waveform features (ANNwaveform), to an N-point moving average method (NPMA) and to existing, validated generalized transfer function (GTF). RESULTS: Estimated aortic SBP for all methods was on average less than 1 mmHg away from measured aortic SBP with the exception of NPMA (difference 2.0 ±â€Š3.5 mmHg, P = 0.62). Variability of the difference was significantly greater in ANNSBP.DBP.HR and ANNSBP.DBP (both SD of ±â€Š5.9 mmHg, P < 0.001 compared with GTF, ±â€Š4.0 mmHg, P < 0.001). Inclusion of waveform features decreased the variability (ANNwaveform ±â€Š3.9 mmHg, P = 0.264). Estimated aortic SBP in all models was correlated with measured SBP, with ANN models providing statistically similar results to the GTF method, only the NPMA being statistically different (P = 0.031). CONCLUSION: These findings indicate that use of radial SBP, DBP, and HR alone can provide aortic SBP estimation comparable with the GTF, albeit with slightly greater variance. Pending noninvasive validation, the technique provides plausible aortic SBP estimation without waveform analysis.