Biomedical Engineering: Applications, Basis and Communications

This paper presents a new non-invasive approach to predict the status of high total cholesterol (TC) level in blood using bioimpedance and the artificial neural network (ANN) techniques. The input parameters for the ANN model are acquired from a non-invasive bioelectrical impedance analysis (BIA) measurement technique. The measurement data were obtained from 260 volunteered participants. A total of 190 subject's data were used for the ANN training purpose and the remaining 70 subject's data were used for model testing. Six parameters from the BIA parameters were found to be significant predictors for TC level in blood using logistic regression analysis. The six input predictors for the ANN modeling are age, body mass index (BMI), body capacitance, basal metabolic rate, extracellular mass and lean body mass. Four ANN techniques such as the gradient descent with momentum, the resilient, the scaled conjugate gradient and the Levenberg–Marquardt were used and compared for predicting the high TC level in the blood. The finding showed that the resilient method was the best model with prediction accuracy, sensitivity, specificity and area under the curve value obtained from the test data were 82.9%, 85.4%, 79.3% and 0.83%, respectively.

Two challenges for G-protein coupled receptor (GPCR)-ligand docking with flexibility by computational method are: (1) how to accurately model the perturbations of the backbone when docking ligand to the receptor and (2) how to sample the backbone near the binding pocket. To address the two challenges, we propose a combined refinement (coREF) approach working on the flexibility of the docking interface by integrating two different refinement protocols based on RosettaLigand (RL) platform. The first refinement protocol generates multiple initial receptors by parallelling different movements to mimic the observed simultaneous fluctuations of backbones near the binding site without ligand conformation. The second refinement protocol adds a backbone refinement step for interface residues with ligand conformation, which extending the RL docking algorithm. To validate the performance of coREF, we refined 20 GPCR Docking 2008/2010 models submitted by participants. Eight of 10 CXCR4 targets and half of D3 targets showed lower LRMSD values than the submitted models. Nine in 12 results were slightly better than those refined using the RL docking algorithm.

Over the past decade, the development of non-enzymatic electrochemical biosensors had thriven at a considerable rate. Compared with the traditional enzymatic electrochemical biosensors, the non-enzymatic electrochemical biosensors have the advantages of higher sensitivity and stability. Recently, plenty of researches have devoted to synthesizing new materials, such as bimetallic nanoparticles, and also develop specific nanostructures on the sensor surface to solve the problem of poisoning and increase the selectivity. This work develops two non-enzymatic glucose sensors that are based on nanostructured Pt–Ir films which were deposited by electrodeposition. Because of the relatively high deposition current density, bubbles produced vigorously on the working electrode surface. This phenomenon results in leaf-like nanostructure formed naturally on the surface of the working electrode and further increased the catalytic reaction area. Besides, as determined by the sampling analysis method that is developed herein, the presented Pt–Ir sensors mitigate the current drifting problem which is easily observed when a constant potential is applied in an amperometric glucose detection. Furthermore, the presented Pt–Ir sensors show high sensitivity and stability in 1X PBS (0.15 M NaCl) at 37°C in the glucose concentration range of 1–12 mM. Therefore, the presented non-enzymatic glucose sensors not only provide great potential in biomedical applications, such as homecare products, but can also be adapted for the biological application, such as continuous cell culture monitoring.

Identifying end-diastole (ED) and end-systole (ES) frames is highly important in the process of evaluating cardiac function and measuring global parameters accurately, such as ejection fraction (EF), cardiac output (CO) and stroke volume (SV). The aim of this study is to develop a new method based on measuring volume changes in left ventricle (LV) during cardiac cycle. For this purpose, the level set method is used both in detecting endocardium border and quantifying cardiac function of all frames. Demonstrating LV volumes at a time displays ED and ES frames and volumes used in calculating required parameters. Since ES and ED frames exist in iso-volumic phases of the cardiac cycle with minimum and maximum values of LV volume signals, such peaks can be utilized in finding related frames. The results obtained from 44 volunteers, as golden values, were validated by an experienced echocardiologist through manual annotation and comparison of EF, CO and SV measurements. These measurements prove the suggested method provides accurate and reliable automatic detection of ED and ES frames.

Integrative modeling of cardiac system is important for understanding the complex biophysical function of the heart]. To this end, multimodal cardiovascular imaging plays an important role in providing the computational domain, the boundary/initial conditions, and tissue function and properties. In particular, the incorporation of blood flow in the physiological models can help to simulate the hemodynamic properties and their effects on cardiac function. In this paper, we present a multimodal framework for quantitative and subject-specific analysis of blood flow in the cardiac chambers, including the left ventricle (LV). The 3D geometries of the LV at different time steps are extracted from medical images using an atlas of LV shape. The motion of the myocardium wall is used to extract the moving boundary data of the computational geometry. The data is used as a constraint for the computational fluid dynamics (CFD). An arbitrary Lagrangian–Eulerian (ALE) finite element method (FEM) formulation is used to derive a numerical solution of the transient dynamic equation of the fluid domain. With this method, simulation results describe detailed flow characteristics (such as velocity, pressure and wall shear stress) in the computational domain. The personalized hemodynamic characteristics obtained with the proposed approach can provide clinical value for diagnosis and treatment of abnormalities related to disturbed blood flow such as in myocardial remodeling and aortic sinus lesion formation.

Pulse wave velocity (PWV) and arterial stiffness index (ASI) are considered as the independent predictors of cardiovascular morbidity and mortality — the effective methods to assess arterial stiffness. A portable noninvasive arterial elasticity detecting system (PNAEDS) was developed based on embedded microprocessor unit. This system can simultaneously measure PWV, ASI and other arteriosclerosis critical risk factors. Algorithm of main critical risk factors and design of the system were described in detail. To verify the performance of the PNAEDS, laboratory prospective validation and clinical trial were launched. Reliability of the system and superiority of improved PWV algorithm were validated in laboratory prospective validation. Subsequently, the authors implemented a comparative clinical trial (60 subjects) between measured values obtained by the PNAEDS and carotid intima-media thickness (IMT) value that Doppler ultrasonography measured. Clinical testing results revealed that the measurement with the PNAEDS was consistent with the measurement with the Doppler ultrasonography. In summary, the system may be used to reliably determine the arteriosclerosis critical risk factors and to the early diagnosis and prevention of cardiovascular disease.

Capacitive electrocardiogram (cECG) measurement is an attractive approach for long-term health monitoring. However, there is little literature available for the implementation of multiple-channel cECG system in standard limb leads. The circuit diagram for such a system is also rarely available in literature. This paper presents a multiple-channel limb-lead cECG system that utilized conductive fabrics as the capacitive sensors. The design criteria and the corresponding circuit diagram are described in detail. The proposed system also incorporates the capacitive driven-body (CDB) circuit to reduce the common-mode power-line interference (PLI). The presented system is verified to be stable by theoretic analysis and long-term experiments. The signals acquired by the presented system are competitive with those by commercially available electrocardiogram (ECG) machines. The feasible size and distance to the subject for the sensor made by conductive fabric have also been evaluated by a series of tests. From the test results, the sensor is suggested to be of greater than 60 cm² in area and not more than 3 mm in distance for cECG measurement.

Amongst the motion detection and correction algorithms during the scanning procedures, data-processing methods are the most frequently proposed solution to detect and correct patient motions. There are different distance metrics which have been used to detect the patient motions using information contained in the projections. Unfortunately, the performance of usually used metrics is low in the case of small motions while detecting the motions with magnitude of 1 pixel and smaller are very important in the accuracy of diagnosis. In this work, a new distance metric, normalized prediction of projection data algorithm (NPPDA) is developed based on the linear prediction filter. The performance of the NPPDA is quantitatively evaluated and compared with usual distance metrics by different experimental studies. A high detection rate is achieved by means of the newly developed distance metric, NPPDA.

The purpose of this study was to apply Monte Carlo (MC) based simulations to design a new collimation system to achieve a larger beam size (2 ± 0.1 cm) and tight penumbra (< 1 mm) with a kV X-ray small animal irradiator. The BEAMnrc and DOSXYZnrc MC-based programs were adapted to simulate the system. First the aperture of the primary collimator was expanded until the desired size of the beam was obtained. Subsequently, the beam was trimmed by reducing the aperture size of the secondary collimator in order to eliminate low energy scattered photons and sharpen the penumbra. Finally, the new collimator was constructed and the resultant dose distributions were evaluated with EBT2 film measurements. From the MC computed dose profiles, a 2.1 cm FWHM (1.9 cm width at 95% of the dose) and a sharp penumbra (< 1.0 mm) at 1.0 cm depth in water were obtained. Dose distribution comparisons between the EBT2 film measurements and the MC calculations showed acceptable agreement. MC-based calculation is an effective tool to expedite the creation of new collimator designs and avoid costly machining.

With the development of widespread Internet access, online game playing has become a popular event. As a result, more attention is being paid to the potentially negative physical and psychological effects on prolonged computer worker or online game player. To date, related physical problems that have been identified include fatigue, physical pain, insomnia, epileptic seizures, and even sudden death. Therefore, midterm break may be necessary for an online game player. This study tries to differentiate the physiological effects of two groups, with and without a midterm break, during a prolonged online game playing. Our experimental results showed that sympathetic system is overwhelming the parasympathetic nervous system significantly in group B without break, while it remains unchanged in group A. Due to the fight or flight response of the sympathetic system, participants with midterm break wanted to play more after event. Participants without break did not feel stressed after a continuous two hours' online game playing. These interesting findings may need further investigation in the future.

A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), variance, Shannon entropy, Lempel–Ziv complexity value, and central and maximum frequencies. In order to reduce the dimensionality of the feature vector, the Wilks' lambda (WL) parameter was used for the selection of the most important variables. The classification of mental tasks was performed using linear discriminant analysis (LDA) and neural networks (NN). Using this method, the average classification over all subjects in database is 91 ± 5% and 87 ± 5% using LDA and NN, respectively. Bit rate was ranging from 0.24 bits/trial up to 0.84 bits/trial. The proposed method allows achieving higher performances in the classification of mental tasks than other traditional methods using the same database. This represents an improvement in the brain-machine communication system.

Adequate muscle recruitment patterns and their consistency are essential to achieve effective posture and force exertion during a karate front kick. The purpose of this study was to evaluate the reaction time (RT), motor time (MT) and total response time (TRT), as well as their correlation during a front kick, and to investigate the muscle recruitment patterns and their consistency during motor time. Fourteen professional karate athletes (age: 23.7 ± 2.6 years; height: 174.6 ± 7.1 cm; mass: 72.8 ± 10.7 kg) participated in the current study. Each subject was instructed to pose in combat stance first and then to use their right leg to kick at an instrumented kicking target as soon as they saw the start signal. Surface electromyograms (EMG) were recorded from 16 muscles. Start and stop signals from the instrumented target were also recorded synchronously to obtain the TRT. Significant correlation between MT and TRT indicated that MT was a primary determinant for the TRT of the kick. The muscle recruitment synergies during karate front kicks were identified. When performing a front kick, the athletes initiated the postural adjustments of the supporting leg prior to the onset of the voluntary kick. A successful motion of the karate front kick was dependent not only on the kicking leg, but also on the supporting leg. Tibialis anterior, biceps femoris and gluteus maximus of the supporting leg were the primary muscles providing stabilization, while the ankle and knee flexors, and tensor fasciae latae of the kicking leg were dominant muscles for the kicking movement. These results provide important information on the patterns and the consistencies of the muscle recruitment for coaching a karate front kick, which should be helpful for a better understanding of the motor control strategies of a karate front kick and for developing a suitable training protocol.