syedzayyan/paper_mill_benchmark huggingface dataset:
ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals

As the heartbeat detection from ballistocardiogram (bcg) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the j-peak of each bcg signal so that beat-to-beat interval can be identified.However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple bcg signals.Methods.This paper develops a deep learning framework based on res net and bidirectional long short-term memory (bi lstm) to conduct beat-to-beat detection of bcg signals.Unlike the existing methods, the proposed network takes multiscale features of bcg signals as the input and, thus, can enjoy the complementary advantages of both morphological features of one bcg signal and rhythm features of multiple bcg signals.Different time scales of multiscale features for the proposed model are validated and analyzed through experiments.Results.The bcg signals recorded from 21 healthy subjects are conducted to verify the performance of the proposed heartbeat detection scheme using leave-one-out cross-validation.The impact of different time scales on the detection performance and the performance of the proposed model for different sleep postures are examined.Numerical results demonstrate that the proposed multiscale model performs robust to sleep postures and achieves an averaged absolute error e abs and an averaged relative error e rel of the heartbeat interval relative to the r-r interval of 9.92 ms and 2.67 ms, respectively, which are superior to those of the state-of-the-art detection protocol.Conclusion.In this work, a multiscale deep-learning model for heartbeat detection using bcg signals is designed.We demonstrate through the experiment that the detection with multiscale features of bcg signals can provide a superior performance to the existing works.Further study will examine the ultimate performance of the multiscale model in practical scenarios, i.e., detection for patients suffering from cardiovascular disorders with night-sleep monitoring.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Clinical Study;
Research Progress of Nanomaterial Mechanics for Targeted Treatment of Muscle Strains in Sports Rehabilitation Training

More and more people are beginning to recognize the important role of intelligent rehabilitation training equipment in rehabilitation treatment and continue to carry out related researches.The use of intelligent robot technology for rehabilitation treatment has been rapidly developed, and it has achieved rapid progress on a global scale.Especially in some developed countries, this field has also received corresponding attention in some developed cities in china in recent years.Mesoporous nanomaterials have unique physical, chemical, and biological properties.Mesoporous nanomaterials can be combined with chemotherapy drugs to minimize the harm caused by chemotherapy drugs to the human body and improve the therapeutic effect.As a result, the cure rate has been improved, and it has shown deep potential in breast cancer chemotherapy.Fifty breast cancer patients were selected as the research objects and randomly divided into a control group and an experimental group, each with 25 cases.The control group was treated with conventional chemotherapeutics, and the experimental group was treated with molecular targeted therapy to compare the treatment effects of the two groups.Studies have shown that the recurrence rate and the occurrence probability of complications in the experimental group are significantly lower than those in the control group.Molecular targeted therapy for breast cancer has obvious effects, which reduces the recurrence rate of complications or diseases, and is less toxic.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Research on System Construction and Strategy of Intelligent Sports in the Implementation of National Fitness

This paper studies the construction and development strategy of intelligent sports system in the context of chinese national fitness program with methods of literature review and model construction.The research shows that there are four dilemmas in the implementation of intelligent sports in national fitness: data security, market monopoly, legal supervision, and product iteration.However, there are also three promoting factors in this regard, including policy guarantee, market demand, and industrial upgrading.Following the principles of scientificity, effectiveness, public welfare, and collaboration, this paper designs a system for intelligent sports in national fitness.The construction of the national fitness intelligent sports system mainly consists of four modules, including basic framework construction, function design, content design, and operation analysis.With the systematic analysis of the status quo of intelligent sports application in national fitness, this paper puts forward intelligent sports development strategies in the implementation of national fitness from four aspects: optimizing the top-level design of government, speeding up industrial transformation and upgrading, constructing market supervision mechanism, and establishing a talent training system.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Review Article;
Research on Sports Health Care Information System Based on Computer Deep Learning Algorithm

There are various problems in diagnosing and treating tumor diseases in significant hospitals.The content includes misjudgement and over-surgery issues.For example, the judgment of pulmonary nodules mainly relies on artificial experience, and most of the artificial experience is too radical.This paper is mainly based on the extensive medical data of significant hospitals, extracts the diagnosis and treatment data and digital images of similar cases from the extensive database, classifies them through the deep learning of the computer, and then proposes the control mechanism and the solution of the doctor’s misjudgement and excessive medical treatment.This method mainly relies on the ct and mri digital images of various types of tumor diseases accumulated in the history of major hospitals.Based on the preliminary judgment of each diagnosis and treatment and the results of surgical and pathological examinations, the accumulation of various types of digital images from the history is used.The features are analysed and extracted, the model is built, and finally, a predictive analysis system for this type of tumor is obtained, which can predict the benign and malignant cases of currently occurring cases and avoid the limitations and instability of artificial experience greatest extent.It is proved by experiments and combined with spearman to remove redundancy.The redundancy removal method svm_rfe is used for dimensionality reduction.The method can timely correct the misjudgement of the doctor’s experience and effectively reduce the instability of the manual, which provides a solution for solving the contradiction between doctors and patients and improving the scientific of diagnosis and treatment.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
Research on Segmentation of Brain Tumor in MRI Image Based on Convolutional Neural Network

Brain tumors are the brain diseases with the highest mortality and prevalence, and magnetic resonance imaging has high‐resolution and multiparameter.As the basis for realizing the quantitative analysis of brain tumors, automatic segmentation plays a vital role in diagnosis and treatment.A new network model is proposed to improve the accuracy of convolutional neural network segmentation of brain tumor regions and control the parameter space scale of the network model.The model first uses a convolutional layer composed of a series of 3d convolution filters to construct a backbone network for feature learning of input 3d mri image blocks.Then, a pyramid structure constructed by a 3d convolutional layer is designed to extract and fuse features of tumor lesions and context information of different scales and then classify the fused feature at the voxel level to obtain segmentation results.Finally, a conditional random field is used to postprocess segmentation results for structured refinement.By designing massive ablation experiments to analyze the sensitivity of the essential modules of the comparison network, the results confirm that our method can better solve the problems faced by the traditional fully connected convolutional neural network.

Journal: BioMed Research International (2022)
Publication types: Research Article;
Research on Resource Allocation and Optimization of Community Intelligent Sports Service for the Elderly Based on Group Intelligence

The mainstream development trend in the era of intelligent sports.At present, with the rapid development of science and technology, it is absolutely wise to combine group intelligence with community intelligent sports services for the elderly.Group intelligence has opened a new era of intelligent sports service.Group intelligence has become an important factor in the development and growth of community intelligent sports service for the elderly and has become a hot topic at present.However, intelligence has encountered difficulties on the road of development.At present, the aging of the population is getting worse and worse, and the elderly have higher and higher requirements for fitness and leisure services, which leads to the need for sports services to be continuously strengthened.The distribution of resources is uneven, the data is not clear enough, and the swarm intelligence algorithm is not perfect.With the adaptation of the elderly to intelligence, more intelligent, concise, and personalized services need to be developed.The most important method is to optimize the swarm intelligence algorithm continuously.In this paper, pso algorithm is optimized and hcsspso algorithm is proposed.Hcsspso algorithm is a combination of pso algorithm and clonal selection strategy, and test simulation experiments, pso algorithm, clpso algorithm, and hcsspso algorithm for comparison.From the experimental results, hcsspso algorithm has better convergence speed and stability, whether it is data or comparison graph.The data optimized by hcsspso algorithm is higher than the original data and the other two algorithms in terms of satisfaction and resource allocation.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Research on Nonlinear Time Series Processing Method for Automatic Building Construction Management

Ing at the nonlinear time series of automatic building construction management, a neural network prediction model is proposed to analyze and process the nonlinear sequence of deformation monitoring number cutter.The specific content of this method is as follows: for the noise problems existing in deformation monitoring data, a wavelet is used to denoise the preprocessing; for the bp network and rbf network commonly used in neural networks, the performance of the two networks is compared and demonstrated by matlab program, which proves that rbf neural network can significantly improve the accuracy of deformation prediction.By comparing the results, the maximum relative error of bp network prediction is 18.59%, while the maximum relative error of rbf network prediction is 29.16%, and the average relative error of 13p network prediction is 7.02%, while the average relative error of rbf network prediction value is 10.5%.The comprehensive error of network prediction is 6.1%, rbf network prediction is 8.52%, the standard deviation rmse of bp network prediction error is 15.347, and that of rbf network prediction error is 21.401, and it shows that the prediction accuracy of bp network is higher than that of rbf network.

Journal: Journal of Control Science and Engineering (2022)
Publication types: Research Article;
Research on Nanoeffect of Penetration and Consolidation under Nonlinear Characteristics of Saturated Soft Clay

In order to systematically study the nanopermeability properties of saturated soft clay under different consolidation pressures and different osmotic pressures, this paper analyzes various loading and unloading conditions that affect the permeability of soil based on the material description equation with displacement as the control variable of large deformation consolidation theory.By summarizing the empirical relationship between permeability coefficient and consolidation pressure and permeability coefficient and void ratio, the infiltration law and seepage failure characteristics of soft clay are revealed.For the soil studied in this paper, a = 0.65, b = 0.001, and q = 3.55 are acceptable.The effect of the initial permeability coefficient on large deformation consolidation is studied, and the necessity and plausibility of considering the nonlinearity of the compression and permeability coefficient when calculating the soft-land base large deformation consolidation is also studied.

Journal: International Journal of Analytical Chemistry (2022)
Publication types: Research Article;
Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm

Chest x-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases.However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem.Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance.In order to extract the correlation features among the diseases, some gcn-based models are introduced to combine the features extracted from the images to make prediction.This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme.However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with gcn, which is named sggcn, to meet the need of efficient computation and considerable accuracy.Sggcn used sgnet-101 as backbone, which is built by shuffle ghost block (huang et al., 2021) to extract features with a low computation cost.In order to make sufficient usage of the information in gcn, a new gcn architecture is designed to combine information from different layers together in gcnm module so that we can utilize various hierarchical features and meanwhile make the gcn scheme faster.The experiment on che xpert datasets illustrated that sggcn achieves a considerable performance.Compared with gcn and res net-101 (he et al., 2015) backbone (test auc 0.8080, parameters 4.7m and flops 16.0b), the sggcn achieves 0.7831 (−3.08%) test auc with parameters 1.2m (−73.73%) and flops 3.1b (−80.82%), where gcn with mobile net (sandler and howard, 2018) backbone achieves 0.7531 (−6.79%) test auc with parameters 0.5m (−88.46%) and flops 0.66b (−95.88%).

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Research of Schema Evolution and Implementation Scheme Optimization in AI-Enabled Embedded Systems

The demand of embedded artificial intelligence system for powerful computing power and diversified application scenarios will inevitably bring some new problems.This paper builds the system dynamics model of embedded system based on artificial intelligence (ai).By analyzing the causal relationship between the elements of the system dynamics model, the state equation is established, and the parameters are estimated and tested.At the same time, the influence of the model simulation experiment on the relevant factors is evaluated.The simulation results show that the proposed model is effective and efficient.

Journal: Wireless Communications and Mobile Computing (2021)
Publication types: Research Article;
Renal Doppler Ultrasound in the Evaluation of Renal Function in Patients with Sepsis

This study was aimed to explore the renal doppler ultrasound in the evaluation of renal function in patients with sepsis.Fifty patients with sepsis or septic shock were classified into the acute kidney injury (aki) group (n =25) and the non-aki group (n =25) according to whether they had aki.The measurements of renal resistance index (rri) and power doppler ultrasound (pdu) were performed on all patients within 7 days of admission to the intensive care unit (icu).The patient’s renal function was assessed.The results showed that the rri of the two groups showed a slight upward trend over time, and the rri of the aki group was higher than that of the non-aki group.After 7 days in aki group, the areas under the receiver operating characteristic (roc) curves of rri were 0.745, 0.683, 0.729, 0.856, 0.793, 0.819, and 0.836 (p &lt;0.05).There were no statistically considerable differences in areas under roc curves between the two groups (p &gt;0.05).The grouping of aki and the time were both fixed effects, and the individual patients were randomized effects.Besides, the linear models were statistically analyzed.The results showed that the differences between the two groups were statistically insignificant (p &gt;0.05).There was no significant difference in the pdu scores measured at different times within 7 days after icu admission between the two groups (p &gt;0.05).In conclusion, renal doppler ultrasound had a good adoption effect in the evaluation of the renal function of patients with severe sepsis, which is worth promoting in clinical practice.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Relaxin-3 Ameliorates Diabetic Cardiomyopathy by Inhibiting Endoplasmic Reticulum Stress

This study is aimed at investigating whether relaxin-3 exhibits protective effects against cardiomyopathy in diabetic rats by suppressing ers.Methods.Eighty male sd rats were randomly divided into two groups: controls ( n = 20 ) and diabetes ( n = 60 ).The streptozotocin-treated rats were randomly divided into three groups: diabetic group (dm), low-dose relaxin-3 group (0.2 μg/kg/d), and high-dose relaxin-3 group (2 μg/kg/d).The myocardial tissues and collagen fiber were observed by hematoxylin and eosin (h&amp;e) and masson staining.Serum brain natriuretic peptide (bnp), troponin (tni), myoglobin, interleukin (il-17), interleukin (il)-1α, and tumor necrosis factor (tnf)-α were determined by elisa.The protein expression of glucose regulatory protein 78 (grp78) and c/ebp homologous protein (chop) in the heart tissue of each group was detected by western blot analysis.Results.(1) he and masson staining indicated that relaxin-3 could attenuate myocardial lesions and myocardial collagen volume fraction.(2) bnp, tn i, and myoglobin in the dm group at four and eight weeks were significantly higher than in the controls ( p &lt; 0.01 ).The relaxin-3-treated groups showed significantly reduced serum bnp, tn i, and myoglobin levels compared with the dm group ( p &lt; 0.05 ).(3) il-17, il-1α, and tnf-α levels in the dm rats at 4 weeks were higher than in the controls ( p &lt; 0.05 ).Low or high dose of relaxin-3-treated groups showed reduced serum il-17 and tnf-α levels compared with the dm group at four and eight weeks ( p &lt; 0.05 ).(4) chop and grp78 protein expression was increased in the dm group at four and eight weeks compared with the controls ( p &lt; 0.01 ), and small and large doses of relaxin-3 significantly reduced grp78 and chop protein expression.Conclusions.Exogenous relaxin-3 ameliorates diabetic cardiomyopathy by inhibiting ers in diabetic rats.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Related Factors and a Threshold of the Maximum Neuron-Specific Enolase Value Affecting the Prognosis of Patients with Aneurysmal Subarachnoid Hemorrhage

The prognosis of patients with subarachnoid hemorrhage is influenced by many factors.Neuron-specific enolase (nse) is a biological marker of neurological damage.This study aimed to determine the related prognostic factors and whether or not the maximum nse value (nsemax) has a threshold between good and poor prognosis in aneurysmal subarachnoid hemorrhage (a sah).Methods.A total of 259 patients admitted following a sah were treated by appropriate methods.Initial neurological severity was evaluated by using the initial glasgow coma scale and hunt–hess grades.Nse plasma concentration was measured during the patient’s stay in the neurosurgical intensive care unit, and nsemax was selected for further study.The primary endpoint of the study was glasgow outcome score (gos), which was dichotomized as poor outcome (gos 1–3) or good outcome (gos 4–5) at discharge.Results.A poor outcome of patients with a sah at discharge was associated with mild hypothermia treatment, hunt–hess grade, rehemorrhagia, neurogenic pulmonary edema, and pneumonia, which were independent risk factors affecting the prognosis of patients.The best threshold of the maximum value of nse for poor or good prognosis was 26.255 μg/l (specificity 0.908).Conclusions.Poor neurological score, pulmonary complications, aneurysm rerupture, and mild hypothermia indicate a poor prognosis.Nsemax&gt;26.255 μg/l is an independent predicting factor of poor neurological outcome at discharge after a sah.This threshold value could help clinicians make the appropriate decision and prognosis.

Journal: Applied Bionics and Biomechanics (2022)
Publication types: Research Article;
Related Factor Analysis and Nursing Strategies of Diarrhea in Critically Ill Patients with Enteral Nutrition

To explore the related factors of diarrhea in critically ill patients with enteral nutrition (en) in the intensive care unit (icu).Methods.This single-center retrospective study analyzed the occurrence of intolerant diarrhea in icu patients receiving en treatment in our hospital.By collecting clinical data, univariate and multivariate logistic regression analysis was used to screen the risk factors for diarrhea.Results.Among 120 patients included in the study, 68 (48.33%) had diarrhea.Age (or = 2.599, p = 0.027 ), use of antibiotics (or = 3.496, p = 0.007 ), icu hospitalization time (or = 1.311, p = 0.001 ), and mechanical ventilation time (or = 1.273, p = 0.035 ) were all independent risk factors for diarrhea in en.Conclusion.Older age, frequent use of antibiotics, long icu stay, and mechanical ventilation time can lead to diarrhea in icu patients receiving en treatment.It is necessary to effectively analyze the above independent factors and implement targeted interventions to improve the incidence of diarrhea in patients.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;