Showing papers in "Journal of Mechanics in Medicine and Biology in 2016"
TL;DR: In this paper, the authors studied the application of drug delivery in magnetohydrodynamics (MHD) peristaltic blood flow of nanofluid in a non-uniform channel.
Abstract: In this paper, we have studied the application of drug delivery in magnetohydrodynamics (MHD) peristaltic blood flow of nanofluid in a non-uniform channel. The governing equation of motion and nanoparticles are modeled under the consideration of creeping flow and long wavelength. The resulting non-linear coupled differential equation is solved with the help of perturbation. Numerical Integration has been used to obtain the results for pressure rise and friction forces. The impact of various pertinent parameters on temperature profile, concentration profile such as density Grashof number, thermal Grashof number, Brownian motion parameter, thermophoresis parameter and MHD is demonstrated mathematically and graphically. The present analysis is also applicable for three-dimensional profile.
70 citations
TL;DR: Recurrence Quantification Analysis (RQA) features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-F fib, AFL and V-Fib using ensemble classifiers.
Abstract: Atrial Fibrillation (A-Fib), Atrial Flutter (AFL) and Ventricular Fibrillation (V-Fib) are fatal cardiac abnormalities commonly affecting people in advanced age and have indication of life-threatening condition. To detect these abnormal rhythms, Electrocardiogram (ECG) signal is most commonly visualized as a significant clinical tool. Concealed non-linearities in the ECG signal can be clearly unraveled using Recurrence Quantification Analysis (RQA) technique. In this paper, RQA features are applied for classifying four classes of ECG beats namely Normal Sinus Rhythm (NSR), A-Fib, AFL and V-Fib using ensemble classifiers. The clinically significant (p<0.05) features are ranked and fed independently to three classifiers viz. Decision Tree (DT), Random Forest (RAF) and Rotation Forest (ROF) ensemble methods to select the best classifier. The training and testing of the feature set is accomplished using 10-fold cross-validation strategy. The RQA coefficients using ROF provided an overall accuracy of 98.37% ag...
62 citations
TL;DR: In this review, the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy is adopted and support.
Abstract: Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.
50 citations
TL;DR: This work has proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals using the Empirical Mode Decomposition (EMD) technique.
Abstract: Coronary Artery Disease (CAD) is a heart disease caused due to insufficient supply of nutrients and oxygen to the heart muscles. Hence, reduced supply of nutrients and oxygen causes heart attack or stroke and may cause death. Also significant number of people are suffering from CAD around the world so timely diagnosis of CAD can save the life of patients. In this work, we have proposed computer assisted diagnosis of CAD using Heart Rate (HR) signals obtained from Electrocardiogram (ECG) signals. We have used the Empirical Mode Decomposition (EMD) technique to process the HR signals. The features namely: Second-Order Difference Plot (SODP) area, Analytic Signal Representation (ASR) area, Amplitude Modulation (AM) bandwidth, Frequency Modulation (FM) bandwidth and Fourier–Bessel expansion (FBE)- based mean frequency computed from the Intrinsic Mode Functions (IMFs) are extracted to discriminate normal and CAD subjects. Thereafter, Kruskal–Wallis statistical test is performed on these features. The features having p-value less than 0.05 are considered to be significant. Our results show that three features namely: AM bandwidth, FM bandwidth and FBE-based mean frequency are more suitable than ASR area and SODP area features for discrimination of normal and CAD subjects.
45 citations
TL;DR: Significance of gait analysis in robotic research is also illustrated in this part where the study focuses on robot assisted systems and its possible applicability in clinical rehabilitation and sports training.
Abstract: Human gait is the identity of a person's style and quality of life. Reliable cognition of gait properties over time, continuous monitoring, accuracy of evaluation, and proper analysis of human gait characteristics have demonstrated their importance not only in clinical and medical studies, but also in the field of sports, rehabilitation, training, and robotics research. Focusing on walking gait, this study presents an overview on gait mechanisms, common technologies used in gait analysis, and importance of this particular field of research. Firstly, available technologies that involved in gait analysis are briefly introduced in this paper by concentrating on the usability and limitations of the systems. Secondly, key gait parameters and motion characteristics are elucidated from four angles of views; one: gait phases and gait properties; two: center of mass and center of pressure (CoM-CoP) tracking profile; three: Ground Reaction Force (GRF) and impact, and four: muscle activation. Thirdly, the study focuses on the clinical observations of gait patterns in diagnosing gait abnormalities of impaired patients. The presentation also shows the importance of gait analysis in sports to improve performance as well as to avoid risk of injuries of sports personnel. Significance of gait analysis in robotic research is also illustrated in this part where the study focuses on robot assisted systems and its possible applicability in clinical rehabilitation and sports training.
45 citations
TL;DR: In this paper, the authors examined heat diffusion in hydromagnetic nanofluids in a peristaltic system, motivated by applications in medical drug delivery systems and gastric magnetographic monitoring.
Abstract: Magnetic nanofluid technologies are emerging in numerous areas including medicine, lubrication (smart tribology), pharmacology, etc. In this paper, we examine heat diffusion in hydromagnetic nanofluids in a peristaltic system, motivated by applications in medical drug delivery systems and gastric magnetographic monitoring. The mathematical formulation encompasses momentum and heat conservation equations with appropriate boundary conditions for compliant walls. Sophisticated correlations are employed for thermal conductivity of the nanoparticles. The nonlinear boundary value problem is normalized with appropriate variables and closed-form solutions are derived for stream function, pressure gradient and temperature profile. Analytical solutions are evaluated numerically with MATHEMATICA symbolic software. Validation of computations is performed for the nonlinear moving boundary value problem via a Chebyschev spectral collocation method (CSM). A detailed study is performed for the influence of various nanoparticle geometries (bricks, cylinders and platelets). With greater magnetic field, flow velocity is enhanced for platelet nanoparticles whereas it is depressed for brick particles. Temperature is dramatically modified with nanoparticle geometry and greater thermal conductivity is achieved with brick-shaped nanoparticles in the fluid, with implications for optimized medical device systems.
39 citations
TL;DR: This work has obtained a classification accuracy of 99.77% using ICs on DWT method and developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.
Abstract: Electrocardiogram (ECG) signal is a non-invasive method, used to diagnose the patients with cardiac abnormalities. The subjective evaluation of interval and amplitude of ECG by physician can be tedious, time consuming, and susceptible to observer bias. ECG signals are generated due to the excitation of many cardiac myocytes and hence resultant signals are non-linear in nature. These subtle changes can be well represented and discriminated in transform and non-linear domains. In this paper, performance of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) methods are compared for automated diagnosis of five classes namely Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) beats. Six different approaches: (i) Principal Components (PCs) on DCT, (ii) Independent Components (ICs) on DCT, (iii) PCs on DWT, (iv) ICs on DWT, (v) PCs on EMD and (vi) ICs on EMD are employed in this work. Clinically significant features are selected using ANOVA test (p<0.0001) and fed to k-Nearest Neighbor (k-NN) classifier. We have obtained a classification accuracy of 99.77% using ICs on DWT method. Consistency of performance is evaluated using Cohen’s kappa statistic. Developed approach is robust, accurate and can be employed for mass diagnosis of cardiac healthcare.
30 citations
TL;DR: The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals by obtaining the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation.
Abstract: Diabetes Mellitus (DM) which is a chronic disease and difficult to cure. If diabetes is not treated in a timely manner, it may cause serious complications. For timely treatment, an early detection of the disease is of great interest. Diabetes can be detected by analyzing the RR-interval signals. This work presents a methodology for classification of diabetic and normal RR-interval signals. Firstly, empirical mode decomposition (EMD) method is applied to decompose the RR-interval signals in to intrinsic mode functions (IMFs). Then five parameters namely, area of analytic signal representation (AASR), mean frequency computed using Fourier-Bessel series expansion (MFFB), area of ellipse evaluated from second-order difference plot (ASODP), bandwidth due to frequency modulation (BFM) and bandwidth due to amplitude modulation (BAM) are extracted from IMFs obtained from RR-interval signals. Statistically significant features are fed to least square-support vector machine (LS-SVM) classifier. The three kernels namely, Radial Basis Function (RBF), Morlet wavelet, and Mexican hat wavelet kernels have been studied to obtain the suitable kernel function for the classification of diabetic and normal RR-interval signals. In this work, we have obtained the highest classification accuracy of 95.63%, using Morlet wavelet kernel function with 10-fold cross-validation. The classification system proposed in this work can help the clinicians to diagnose diabetes using electrocardiogram (ECG) signals.
29 citations
TL;DR: A three dimensional unsteady state model of Calcium dynamics in a neuron cell has been developed and it helps to have more realistic study of calcium diffusion in neuron cells.
Abstract: The study of calcium diffusion in neuron cells has gained interest among research workers during the last two decades, due to its wide variety of roles in human body like muscle contraction, secretion, metabolism, signal transduction etc. Na+ is the first ion that comes in the hierarchy of ions affecting cytosolic Ca2+ concentration. This Na+ ion helps in intracellular Ca2+ regulation in cytosol via Na+/Ca2+ exchanger protein (NCX protein). Most of the theoretical investigations on calcium diffusion in neuron cells have been carried out for one and two dimensional cases by various research workers and that too by incorporating a point source of influx. In order to have more realistic study the more details of geometry, microstructure and physiological parameters need to be incorporated in the models. In view of above a three dimensional unsteady state model of Calcium dynamics in a neuron cell has been developed. Apart from the point source, the line and surface sources of an influx of Ca2+as well as the ...
28 citations
TL;DR: In this article, the effect of induced magnetic field on blood flow through a constricted channel was analyzed by taking valid numerical values of the parameters, which are applicable to blood rheology.
Abstract: A nonlinear micropolar fluid model is considered with a view to examine the effect of induced magnetic field on blood flow through a constricted channel. We assume that the flow is unidirectional and flowing through a narrow channel, where the Reynolds number is less than unity such as in microvessels. Under the low Reynolds number approximation, the analytical expressions for axial velocity, micro-rotation component, axial pressure gradient, axial induced magnetic field, resistance to flow and wall shear stress have been obtained. The flow characteristic phenomena have been analyzed by taking valid numerical values of the parameters, which are applicable to blood rheology. The present analytical solutions have been compared with the analytical solutions of Hartmann (Hartmann J. Hg-Dynamics-I: Theory of the laminar flow of an electrically conductive liquid in a homogeneous magnetic field, Mathematisk-Fysiske MeddeleserXV:6, 1937) and found excellent agreement. The study shows that with the increasing values of the magnetic field strength decreases the axial velocity at the central line of the channel, while the flow is accelerating in the vicinity of the channel wall. The induced magnetic field has an increasing effect on the micro-rotation component, which in turn produces increasing pressure gradient. The electrical response of the microcirculation increases with the increase in the Hartmann number and the stenosis height. Thus, the resultant flow predictions presented here may be useful for the potential applications in cardiovascular engineering.
22 citations
TL;DR: A proposed automatic classification system of normal and depression EEG signals can serve as a useful diagnostic and monitoring tool for detection of depression.
Abstract: Depression is a mental disorder that relates to a state of sadness and dejection. It also affects the emotional and physical state of a person. Currently, there are no standard diagnostic tests for depression that are able to produce conclusive results and more over the symptoms of depression are hard to diagnose. A lot of people who are suffering from depression are unaware of their illness. The electroencephalographic (EEG) signals can be used to detect the alterations in the brain's electrochemical potential. The present work is based on the automated classification of the normal and depression EEG signals. Thus, signal processing methods are used to extract hidden information from the EEG signals. In this work, normal and depression EEG signals are used and discrete wavelet transform (DWT) is performed up to two levels. The features (skewness, energy, kurtosis, standard deviation (SD), mean and entropy) are extracted at the various detailed coefficients levels of the DWT. The extracted features then undergo a statistical analysis method, which is the Student's t-test that determines the significance of differences in the features. Support Vector Machine classifier with Radial Basis Kernel Function (SVM RBF) was used and the classification accuracy results of 88.9237% was obtained. Hence, this proposed automatic classification system can serve as a useful diagnostic and monitoring tool for detection of depression.
TL;DR: The computations demonstrate that velocity, flow rate and shear stress increase while resistance to flow decreases with greater permeability parameter, demonstrating the powerful utility of exploiting magnetic fields in hemodynamic flow control.
Abstract: Unsteady pulsatile flow of blood through a porous-saturated, tapered and overlapping stenotic artery in the presence of magnetic field is examined theoretically and computationally. The power law constitutive model is employed to simulate hameo-rheological characteristics. The governing equation is derived assuming the flow to be unsteady, laminar, uni-directional and one-dimensional (1D). A robust, finite difference method is employed for the solution of the governing equation, subject to appropriate boundary conditions. Based on this solution, an extensive quantitative analysis is performed to analyze the effects of blood rheology, body acceleration, magnetohydrodynamic parameter, permeability parameter and arterial geometrical parameters of stenosis on various quantities of interest such as axial velocity, flow rate, resistance impedance and wall shear stress. The computations demonstrate that velocity, flow rate and shear stress increase while resistance to flow decreases with greater permeability parameter. Additionally, the effects of magnetic field are observed to be converse to those of permeability i.e., flow is decelerated and resistance is increased, demonstrating the powerful utility of exploiting magnetic fields in hemodynamic flow control (e.g., intra-corporeal surgical procedures). Furthermore, the size of trapped bolus of fluid is also found to be reduced for large values of the permeability parameter indicating that progressively more porous media circumvent bolus growth.
TL;DR: In this paper, a CFD simulation is carried out to investigate the fluid mechanics and performance of needle free injectors powered specifically by compressed air, and numerical results are discussed by comparing the fluid stagnation pressures of the liquid jet with previously published experimental measurements obtained using a custom-built prototype of the air-powered needle free liquid injector.
Abstract: A liquid jet injector is a biomedical device intended for drug delivery. Medication is delivered through a fluid stream that penetrates the skin. This small diameter liquid stream is created by a piston forcing a fluid column through a nozzle. These devices can be powered by springs or compressed gas. In this study, a CFD simulation is carried out to investigate the fluid mechanics and performance of needle free injectors powered specifically by compressed air. The motion of the internal mechanisms of the injector which propels a liquid jet through an orifice is simulated by the moving boundary method and the fluid dynamics is modeled using LES/VOF techniques. In this paper, numerical results are discussed by comparing the fluid stagnation pressures of the liquid jet with previously published experimental measurements obtained using a custom-built prototype of the air-powered needle free liquid injector. Performance plots as a function of various injector parameters are presented and explained.
TL;DR: This paper aims to detect automatically the stress user when he is interacting with computer by using instantaneous pulse rate signal extracted from imaging photoplethysmography to elicit emotional stress in the subject.
Abstract: One of the goals of affective computing field is to provide to computers the ability to recognize automatically the affective state of the user in order to have more intuitive human–machine communication. This paper aims to detect automatically the stress user when he is interacting with computer. The developed system is based on instantaneous pulse rate (PR) signal extracted from imaging photoplethysmography (PPG). Seven features from time and frequency domain are extracted from PR signal and processed by learning pattern recognition systems. Two methods based on Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used and compared to classify the user’s emotional state. A computer application based on “Stroop color word Test” is developed to elicit emotional stress in the subject. The proposed method can achieve the overall average classification accuracy of 94.42% and 91.10% with SVM and LDA, respectively. Current results indicate that our approach is effective for stress classification.
TL;DR: This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality, and aims to instill trust in CAD of cardiovascular diseases using ECG signals.
Abstract: The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.
TL;DR: The peristaltic flow of a carbon nanotubes (CNTs) water fluid investigate the effects of heat generation and magnetic field in permeable vertical diverging tube is studied in this article.
Abstract: The peristaltic flow of a carbon nanotubes (CNTs) water fluid investigate the effects of heat generation and magnetic field in permeable vertical diverging tube is studied. The mathematical formulation is presented, the resulting equations are solved exactly. The obtained expressions for pressure gradient, pressure rise, temperature, velocity profile are described through graphs for various pertinent parameters. The streamlines are drawn for some physical quantities to discuss the trapping phenomenon. It is observed that pressure gradient profile is decreasing by increase of Darcy number Da, becausy Darcy number is due to porous permeable walls of the tube and when walls are porous fluid cannot easily flow in tube, so that will decreases the pressure gradient.
TL;DR: It is found that ApEn can be used as the evaluation criteria of cognitive workload, which could be applied in the ergonomics estimation of human-interface interaction field.
Abstract: The traditional cockpit display-control system usually has great many instruments and much complex information, which leads to the pilots to take a long time to be familiar with the cockpit interface and often cause accidents when emergencies happen. Thus it is necessary to evaluate the cognitive workload of the pilots under multitask conditions. A simplified evaluation method of cognitive workload by approximate entropy (ApEn) of electroencephalography (EEG) is proposed in this paper. We design a series of experiments about the flight instruments, which have different instrument number, pointer speed, and operation difficulty, and collect the EEG, interval time (IT), and misjudgment rate (MR), then classify and analyze these data with ApEn algorithm, traceability, and dualistic linear regression method. It can be found that ApEn is increased with increasing experiment difficulty, which shows that ApEn can be used as the evaluation criteria of cognitive workload. As the ApEn and the number of dipoles have a positive correlation relationship, the cognitive workload and ApEn are both changed with increasing the number of brain dipoles. Taking MR and IT as the independent variables, and ApEn as the dependent variable, we obtain an empirical formula to simplify the assessment process of the cognitive workload. This study concludes that ApEn can be used as the evaluation criteria of cognitive workload, which could be applied in the ergonomics estimation of human-interface interaction field.
TL;DR: In this article, a novel and facile method to fabricate the drag-reducing surface on a semi-cured coating based on biological sharkskin is investigated and explored, where sputtering and photo lithography processes are put into application to eliminate the wedge angle on scale's back.
Abstract: The artificial drag-reducing surface with biological sharkskin morphology was fabricated by the direct bio-replicated method, and the satisfactory drag reduction effect was validated in the water tunnel. However, the splicing step is necessary for the area larger than the entire surface of shark, the consequence of which is that the complexity of process will be increased seriously. At the meanwhile, two adverse effects may appear, namely, the stress contraction on the jointing seams and the drop-out phenomenon. Therefore, it is urgent to manufacture the continuous biomimetic sharkskin surface. In this paper, a novel and facile method to fabricate the drag-reducing surface on a semi-cured coating based on biological sharkskin is investigated and explored. Firstly, the sputtering and photo lithography processes are put into application to eliminate the wedge angle on scale’s back. Secondly, the relationship between forming precision and curing degree of epoxy resin is inspected and the appropriate time-zon...
TL;DR: It is concluded that the novel bioreactor introduced here, has the potential to be easily applied for cartilage tissue engineering on a larger scale.
Abstract: In the present study, a novel bioreactor for dynamic hydrostatic pressure loading that simultaneously permits medium perfusion was established. This bioreactor enables continuous cultivation without manual attendance. Additional emphasis was placed on a simple bioreactor design which was achieved by pressurizing the medium directly and by applying pressure loading and perfusion through the same piping. Straight forward pressure control and at the same time maintaining sterility were achieved by using a peristaltic pump including inlet and outlet magnetic pinch valves connected with a real-time control. Cell tests using chondrocytes were performed and similar cell proliferation rates in the bioreactor and in the incubator were found. We conclude that the novel bioreactor introduced here, has the potential to be easily applied for cartilage tissue engineering on a larger scale.
TL;DR: In this paper, a multi-degree of freedom exoskeleton robot, with light weight, including (6+1) DOFs, named as Rehab-Arm, is proposed and developed for upper limb rehabilitation.
Abstract: Patients who suffer from stroke have motion function disorders. They need rehabilitation training guided by doctors and trainers. Nowadays, robots have been introduced to help the patients regain their motion function in rehabilitation training. In this paper, a novel multi degree of freedom (DOF) exoskeleton robot, with light weight, including (6+1) DOFs, named as Rehab-Arm, is proposed and developed for upper limb rehabilitation. The joints of the robot are equipped with micro motors which are capable of actuating each DOF respectively and simultaneously. The medial/lateral rotation of shoulder is realized by a semi-circle guide mechanism for convenience consideration and safety. The robot is used in sitting posture which is attached to a custom made chair. Hence, the robot can be used to assist patients in passive movement with 7 DOFs of the upper limb for rehabilitation. Five adult healthy male subjects participated in the experiment to test the joint movement accuracy of the robot. Finally, subjects can wear Rehab-Arm and move their upper limb, led by micro motors of the robot, to perform task assigned with specific trajectory.
TL;DR: Although ML Peak and VV have shown rising trend from normal people to severe KOA, ML difference was not significant in various groups (p>0.05), and the VP1 did not differ significantly among the subjects, the relationship between various severity of KOA and the forces applied to the lower limb during walking was investigated.
Abstract: Knee osteoarthritis (KOA) changes the force applied on the lower extremities. The purpose of this study was to investigate the relationship between various severity of KOA and the forces applied to the lower limb during walking. Sixty eight limbs were divided into three groups of mild, moderate and severe KOA and a healthy normal group according to the Kellgren–Lawrence scale. The subjects walked with a self-selected speed to collect five successful trials. The components of ground reaction forces i.e., medio-lateral (ML), first peak of antero-posterior (AP1), second peak of antero-posterior (AP2), first peak of vertical (VP1), second peak of vertical (VP2) and vertical valley (V.V) were collected. AP1 and AP2 had decreasing pattern with increasing disease severity. Although ML Peak and VV have shown rising trend from normal people to severe KOA, ML difference was not significant in various groups (p>0.05). In addition, the VP1 did not differ significantly among the subjects. In contrast, the VP2 decrease...
TL;DR: This study aimed to investigate the appropriate distance and clearance between impellers and diffusers of axial blood pumps, which contains the best low hemolytic property and hydraulic performance using the computational fluid dynamics (CFDs) approach.
Abstract: Low hemolysis and hydraulic performance are important factors for an axial blood pump, which have been transplanted in patients with heart failure (HF). The distance and clearance between impeller and diffuser play a key role in hemolytic properties and hydraulic performances of axial blood pumps that were developed by our group inspired by the design features of HeartMate II. In the present study, we aimed to investigate the appropriate distance and clearance between impellers and diffusers of axial blood pumps, which contains the best low hemolytic property and hydraulic performance using the computational fluid dynamics (CFDs) approach. Specially, the hemolysis of the pump was calculated by using two different empirical power-law hemolytic blood damage models with two sets of parameters. The two hemolytic blood damage models with two sets of parameters were analyzed and compared. Further, the different distances and clearances between impellers and diffusers that affect hemolytic and hydraulic characte...
TL;DR: In this paper, the authors investigated the mass flow rate through a throttle valve under different operating conditions and found that when the valve opening increases, the vortices grow and cause higher pressure drop, and more energy is lost due to these growing vortice and high viscosity of biofluid.
Abstract: Biofluid flow through a throttle valve is investigated numerically and experimentally in our paper. Numerical studies are performed in order to obtain the mass flow rate through the valve under different operating conditions. Pressure drop behind the throttle valve and formation of the vortex flow downstream has been evaluated. The vortices were mainly distributed on top of the valve rod, the corner of the channel and the corner of the valve seat. When valve opening increases, the vortices grow and cause higher pressure drop. In other words, more energy is lost due to these growing vortices and high viscosity of biofluid. Furthermore, experimental flow visualization is conducted to capture cavitation images near the orifice using high-speed camera. The initial position of cavitation occurred near throttle orifice while cavitation zone downstream is caused by circulating bubbles clusters. As the opening of the valve is decreased, the area and strength of vortices in the corner of the channel grow and cause higher pressure drop firstly, then decrease. In addition, there are a lot of bubble clusters on top of the valve rod and the corner of the valve seat, which flowed downstream and collapsed, then filled the entire channel. In general, the valve opening plays very important role in the performance of a throttle valve. The results would help to observe, understand and manage the cavitation phenomenon in a throttle valve, and improve the performance of throttle valves.
TL;DR: An automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors, which observed a unique classification pattern between low risk and high risk images.
Abstract: Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes 7×15, 15×7, 7×3, and 3×7 pixels, and two noncentered areas with sizes 7×3 pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex (31.2%±14.4%) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images (18.2%±11.8%; p<0.001). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.
TL;DR: This study addresses use of sEMG signals acquired from upper extremities to predict onset of muscle fatigue using deep belief networks (DBNs) as a learning mechanism and demonstrates that a deep architecture can learn from raw data and provide comparable performance to feature-based approaches.
Abstract: In recent years, a robust increasing interest has been observed in wearable devices featuring smart health, smart fitness, and human–machine interaction applications. While we gained some advances on use of surface electromyography (sEMG) signals recorded from upper extremities for controlling external devices, only limited attempt has been made to track the status of targeted muscles and forecast muscle fatigue onset. In this study, we address use of sEMG signals acquired from upper extremities to predict onset of muscle fatigue using deep belief networks (DBNs) as a learning mechanism. We demonstrate that a deep architecture can learn from raw data and provide comparable performance to feature-based approaches. Experimental results show that the DBNs model investigated in this study achieves an average classification accuracy of 85.3% without any subject-oriented calibration and achieves a best case accuracy of 97.60%. A transient-to-fatigue state is introduced before the fatigue onsets as an early warning state. The aim of this paper is to evaluate the performance of the popular deep models in real fatigue detection applications. The model provides a promising result compared with state-of-art works without any feature selection process, which could potentially generate better features while reducing the requirement for expertise in data.
TL;DR: A constitutive model is illustrated to describe the short-term mechanical response of PermacolTM bioprostheses to address the development of numerical models to evaluate the biomechanical performance of the graft with surrounding host tissues.
Abstract: Bioprostheses obtained from animal models are often adopted in abdominal surgery for repair and reconstruction. The functionality of these prosthetic implants is related also to their mechanical characteristics that are analyzed here. This work illustrates a constitutive model to describe the short-term mechanical response of PermacolTM bioprostheses. Experimental tests were developed on tissue samples to highlight mechanical non-linear characteristics and viscoelastic phenomena. Uni-axial tensile tests were developed to evaluate the strength and strain stiffening. Incremental uni-axial stress relaxation tests were carried out at nominal strain ranging from 10% to 20% and to monitor the stress relaxation process up to 400s. The constitutive model effectively describes the mechanical behavior found in experimental testing. The mechanical response appears to be independent on the loading direction, showing that the tissue can be considered as isotropic. The viscoelastic response of the tissue shows a strong decay of the stress in the first seconds of the relaxation process. The investigation performed is aimed at a general characterization of the biomechanical response and addresses the development of numerical models to evaluate the biomechanical performance of the graft with surrounding host tissues.
TL;DR: It is shown that the prediction capability of seven machine learning classifiers can be enhanced by integrating combinations of observed co-expressed features and the proposed voting mechanism achieved optimal performance according to TOPSIS.
Abstract: Background: Coronary artery disease (CAD) is one of the most representative cardiovascular diseases. Early and accurate prediction of CAD based on physiological measurements can reduce the risk of heart attack through medicine therapy, healthy diet, and regular physical activity. Methods:Four heart disease datasets from the UC Irvine Machine Learning Repository were combined and re-examined to remove incomplete entries, and a total of 822 cases were utilized in this study. Seven machine learning methods, including Naive Bayes, artificial neural networks (ANNs), sequential minimal optimization (SMO), k-nearest neighbor (KNN), AdaBoost, J48, and random forest, were adopted to analyze the collected datasets for CAD prediction. By combining co-expressed observations and an ensemble voting mechanism, we designed and evaluated a new medical decision classifier for CAD prediction. The TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) algorithm was applied to determine the best prediction method for CAD diagnosis. Results: Features of systolic blood pressure, cholesterol, heart rate, and ST depression are considered to be the most significant differences between patients with and without CADs. We show that the prediction capability of seven machine learning classifiers can be enhanced by integrating combinations of observed co-expressed features. Finally, compared to the use of any single classifier, the proposed voting mechanism achieved optimal performance according to TOPSIS.
TL;DR: In this article, the authors compared the performance of different stents with different link structures and found that S-type or U-type stents, with larger plastic strain and lower von Mises stress on the arteries, provided the optimal outcome.
Abstract: Different stent structures lead to different deformations of blood vessels, such as different cross-section shapes, which further influence the blood flow patterns. In this paper, sis non-commercial stents with different link structures called I-, C-, S-, U-, N-, and W-types were considered. Their influences on arteries with five different curvatures (i.e., 0 ∘,15 ∘ , 30 ∘, 45 ∘, and 60∘) were studied using finite element method. Four indices including the maximum plastic strain of stents, the rate of expansion, the maximum von Mises stress and the ellipticity of arteries, were compared for all cases. The results showed that the S-type or U-type stents, with larger plastic strain and lower von Mises stress on the arteries, provided the optimal outcome. As the link structures became complex, the arterial expansion increased monotonically, while the ellipticity of arteries showed a decreasing tendency in the vessel models. The present study could be useful for the commercial design and clinic selection of a stent with different link structures for different curved arteries.
TL;DR: Understanding the biomechanical adaptations during pregnancy may provide more information about mechanical loads, which subsequently will be helpful for prescribing exercise and rehabilitation programs, as well as for the prevention of musculoskeletal injuries.
Abstract: Most of the anatomical changes related to the body of pregnant women occur between the second and third trimesters of pregnancy. The purposes of the study were to quantify the lower limb kinetics of gait and draw a comparison between women in the second and third trimesters of pregnancy, and a nonpregnant group. Subjects and methods: A three-dimensional (3D) kinetic analysis of gait was performed in 24 pregnant and 12 nonpregnant women. Results: Between trimesters of pregnancy, a decrease in the third peak of vertical ground reaction force (GRF) in the third trimester was observed. Most of the changes found between pregnant and nonpregnant women were in the sagittal plane for hip, knee and ankle moments, which report a decrease in mechanical load of the lower limb. In frontal plane a significant decrease in ankle joint moment was found, and in the transverse plane a significant increase in hip moment was found. Joints power decreases for hip and ankle power in sagittal and frontal plane, and increases for hip power in transverse plane. The function of propulsion and mobilization appears to be related to the different changes that occur between the right leg and left. Conclusion: These results suggest that adaptations regarding muscle participation occur first (second trimester), followed by adaptations in muscle power (third trimester). Understanding the biomechanical adaptations during pregnancy may provide more information about mechanical loads, which subsequently will be helpful for prescribing exercise and rehabilitation programs, as well as for the prevention of musculoskeletal injuries.
TL;DR: In this article, the effects of heat and mass transfer on peristaltic motion of magnetohydrodynamic (MHD) viscous fluid in a symmetric channel in the presence of Hall and ion-slip currents, viscous and Joule dissipations, and variable temperature-dependent viscosity were investigated.
Abstract: In this paper, we investigate the effects of heat and mass transfer on peristaltic motion of magnetohydrodynamic (MHD) viscous fluid in a symmetric channel in the presence of Hall and ion-slip currents, viscous and Joule dissipations, and variable temperature-dependent viscosity. The governing field equations are solved using series solution under the normal assumptions of long wavelength and low Reynolds number. The pumping characteristics are obtained using numerical integration. The results are critically analyzed for the physical parameters that characterize the peristaltic motion. These parameters include amplitude ratio, volume flow rate, viscosity parameter, Brinkman number, heat generation parameter, magnetic parameter, Hall parameter and ion-slip parameter. The results are presented graphically to understand the behavior of the field quantities and the physics of peristaltic transport of physiological and industrial fluids. Special emphasis has been given to analyze the effects of heat transfer with viscous and Joule dissipations — essentially the new features added in this study.