An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention.

In the field of rehabilitation, although deep learning have been widely used in multitype gesture recognition via surface electromyography (sEMG), their higher algorithmic complexity often leads to low computationally inefficient, which compromise their practicality. To achieve more efficient multitype recognition, We propose the Residual-Inception-Efficient (RIE) model, which integrates Inception and efficient channel attention (ECA). The Inception, which is a multiscale fusion convolutional module, is adopted to enhance the ability to extract sEMG features.

CCMNet: Cross-scale correlation-aware mapping network for 3D lung CT image registration.

The lung is characterized by high elasticity and complex structure, which implies that the lung is capable of undergoing complex deformation and the shape variable is substantial. Large deformation estimation poses significant challenges to lung image registration. The traditional U-Net architecture is difficult to cover complex deformation due to its limited receptive field.

Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging.

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for pathological images, is crucial for data augmentation, clinical disease diagnosis, and understanding pathology-induced changes. Recently, Generative Adversarial Networks (GANs) have shown substantial promise in this domain. However, the heterogeneity of intracranial infection symptoms caused by various infections complicates the model's ability to accurately differentiate between pathological and healthy regions, leading to the loss of critical information in healthy areas and impairing the precise preservation of the subject's identity.

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.

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

. 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.

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.

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.

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.

[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.

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.

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.

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.

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 (SV) was proposed.

[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.

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.

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).

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.

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.

PWPSim: A new simulation tool of pulse wave propagation in the human arterial tree.

Hemodynamic simulation enhances investigations of pulse wave propagation phenomena and enables validation of new methods of pulse wave analysis. However, such simulation systems or tools are not readily available nor easily accessible. In this study, a new simulation tool of pulse wave propagation in the human arterial tree was developed based on a transmission line model (TLM).

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.

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.

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.

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.

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.

A novel method of artery stenosis diagnosis using transfer function and support vector machine based on transmission line model: A numerical simulation and validation study.

Background And Objective: Transfer function (TF) is an important parameter for the analysis and understanding of hemodynamics when arterial stenosis exists in human arterial tree. Aimed to validate the feasibility of using TF to diagnose arterial stenosis, the forward problem and inverse problem were simulated and discussed.

Methods: A calculation method of TF between ascending aorta and any other artery was proposed based on a 55 segment transmission line model (TLM) of human artery tree.