Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
Sudden cardiac arrest is one of the critical clinical syndromes in emergency situations. A cardiopulmonary resuscitation (CPR) is a necessary curing means for those patients with sudden cardiac arrest. In order to simulate effectively the hemodynamic effects of human under AEI-CPR, which is active compression-decompression CPR coupled with enhanced external counter-pulsation and inspiratory impedance threshold valve, and research physiological parameters of each part of lower limbs in more detail, a CPR simulation model established by Babbs was refined. The part of lower limbs was divided into iliac, thigh and calf, which had 15 physiological parameters. Then, these 15 physiological parameters based on genetic algorithm were optimized, and ideal simulation results were obtained finally.
The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters, an optimization design for existing pump blades is conducted. The number of blades, outlet angle, and blade thickness were selected as design variables, with the maximum shear stress within the pump serving as the optimization objective. A back propagation (BP) neural network prediction model was established using existing simulation data, and a grey wolf optimization algorithm was employed to optimize the blade parameters. The results indicated that the optimized blade parameters consisted of 7 impeller blades, an outlet angle of 25 °, and a blade thickness of 1.2 mm; this configuration achieved a maximum shear stress value of 377 Pa—representing a reduction of 16% compared to the original model. Simulation analysis revealed that in comparison to the original model, regions with high shear stress at locations such as the outer edge, root, and base significantly decreased following optimization efforts, thus leading to marked improvements in hemolytic performance. The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation, while also enhancing the performance of optimization objectives. Compared to traditional optimization algorithms, it demonstrates distinct advantages, thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.
The rotation center of traditional hip disarticulation prosthesis is often placed in the front and lower part of the socket, which is asymmetric with the rotation center of the healthy hip joint, resulting in poor symmetry between the prosthesis movement and the healthy lower limb movement. Besides, most of the prosthesis are passive joints, which need to rely on the amputee’s compensatory hip lifting movement to realize the prosthesis movement, and the same walking movement needs to consume 2–3 times of energy compared with normal people. This paper presents a dynamic hip disarticulation prosthesis (HDPs) based on remote center of mechanism (RCM). Using the double parallelogram design method, taking the minimum size of the mechanism as the objective, the genetic algorithm was used to optimize the size, and the rotation center of the prosthesis was symmetrical with the rotation center of the healthy lower limb. By analyzing the relationship between the torque and angle of hip joint in the process of human walking, the control system mirrored the motion parameters of the lower on the healthy side, and used the parallel drive system to provide assistance for the prosthesis. Based on the established virtual prototype simulation platform of solid works and Adams, the motion simulation of hip disarticulation prosthesis was carried out and the change curve was obtained. Through quantitative comparison with healthy lower limb and traditional prosthesis, the scientificity of the design scheme was analyzed. The results show that the design can achieve the desired effect, and the design scheme is feasible.
Stress distribution of denture is an important criterion to evaluate the reasonableness of technological parameters, and the bite force derived from the antagonist is the critical load condition for the calculation of stress distribution. In order to improve the accuracy of stress distribution as much as possible, all-ceramic crown of the mandibular first molar with centric occlusion was taken as the research object, and a bite force loading method reflecting the actual occlusal situation was adopted. Firstly, raster scanning and three dimensional reconstruction of the occlusal surface of molars in the standard dental model were carried out. Meanwhile, the surface modeling of the bonding surface was carried out according to the preparation process. Secondly, the parametric occlusal analysis program was developed with the help of OFA function library, and the genetic algorithm was used to optimize the mandibular centric position. Finally, both the optimized case of the mesh model based on the results of occlusal optimization and the referenced case according to the cusp-fossa contact characteristics were designed. The stress distribution was analyzed and compared by using Abaqus software. The results showed that the genetic algorithm was suitable for solving the occlusal optimization problem. Compared with the reference case, the optimized case had smaller maximum stress and more uniform stress distribution characteristics. The proposed method further improves the stress accuracy of the prosthesis in the finite element model. Also, it provides a new idea for stress analysis of other joints in human body.
Meta-analysis has been regarded as the critical tool of assisting the healthcare professionals to make decisions. And the theory of evidence-based medicine is widely disseminated in domestic. However, it must be noted that the increasing number of meta-analyses causes a fact that several meta-analyses investigating the same or similar clinical questions were captured commonly. More importantly, the results from these meta-analyses are often conflicting. Consequently, decision-making of those healthcare professionals who depend on those results become a thorny thing. To address this issue, Jadad et al. from McMaster University proposed an adjunct algorithm to help healthcare professionals to select the best result from conflicting meta-analyses to make decisions properly. Our article will introduce the tool briefly and explain the process of it with an example.
The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.
Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.
The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.
