The imprudent use of antibiotics has led to increased bacterial resistance, posing a serious threat to public health. Traditional visual-based antibiotic susceptibility testing (AST) methods are time-consuming and labour-intensive. Deep learning-based bacterial detection algorithms have significantly improved the efficiency and accuracy of AST. However, existing deep learning-based bacterial detection algorithms ignore unique challenges of AST images, such as confluent clusters, complex backgrounds and morphologically similar noise. To address these issues, we propose a novel bacterial detection framework named the Triple-stage Contrastive Learning framework with Across-Growth-Stage information (TrCL-AGS). Specifically, we design a novel Across-Growth-Stage Pre-training (AGSP) task to mine across-growth-stage information from the continuous images, providing a richer and more robust feature space for subsequent stages. Instance-level Contrastive Learning (ILCL) is constructed to extract invariant semantic features of bacteria at a particular growth stage and exclude features similar to bacteria but do not match the specific growth stages. Besides, discriminative features between objects and backgrounds are enhanced by the Category-level Contrastive Learning (CLCL) stage. Extensive experiments on a real-world clinical bacterial AST dataset demonstrate that our method can achieve state-of-the-art performance compared with existing object detection methods. And our framework is compatible with most detectors.

The automatic diagnosis of heart sounds is particularly important for cardiologists. However, the existing diagnostic methods still have a large space to be improved, In this paper, we proposed a novel method for heart sound classification. Our method consists of two stages. In the first stage, we preprocessed the heart sound signal, including two steps of denoising and downsampling, to reduce the noise and decrease the complexity of processing. In the second stage, we classify the processed signal, including framing and input network, and finally output three types of results. Our method was validated on the CirCor DigiScope Phonocardiogram Dataset. The result shows the F1 score reached 0.922 and is better compared to other networks’ results.

Cardiovascular disease is the most important cause of death in the world. In the early stage of cardiovascular disease, arrhythmia is often accompanied. Therefore, it is of great significance to carry out safe and effective arrhythmia detection for prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the algorithmic paradigm of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias. In this study, we propose a multi-label classification network based on the multi-scale feature fusion module for arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include group convolution block, Spatial Pyramid Pooling (SPP) layer, and multi-scale feature fusion module. The proposed method can extract features from every lead separately for 12-lead varied-length records, and effectively achieve multi-scale feature extraction and cross-scale information complementarity of ECG by integrating multiple convolution kernels with different receptive fields. The experimental results show that our model achieved an overall classification F1 score of 83.3%. Combining all these excellent features, this model offers a solution for varied-length signal processing problems.

Screening vascular access dysfunction in hemodialysis requires tools that are objective and efficient. Listening for bruits during a physical exam is a subjective examination that can detect stenosis also called vascular narrowing when properly performed. Phonoangiograms (PAGs) which is a mathematical analysis of bruits increase the objectivity and sensitivity and permit quantification of stenosis. In this paper, we proposed Vision Transformer (ViT) for PAGs to automatically classify vascular stenosis. In particular, we added an auxiliary waveform to improve the classification performance. Moreover, we used the method of inter-patient verification to verify the performance of the proposed method. The experimental results show that the total F1 score, recall, and precision of the proposed method are 0.984, 1.000, and 0.969 respectively. We believe the method proposed in this paper has the potential to provide a reference for subsequent research.

ECG is an important medium for doctors to observe the working state of the patient’s heart, and the monitoring of patients based on ECG is very important in clinical diagnosis. The movement of the patient or the activities of other physiological organs in the process of collecting ECG will bring a lot of noise to the ECG signal acquisition, so it is necessary to de-noise the ECG signal with noise. In this paper, an U-net network based on Style-based Recalibration Module (SRM) channel attention is proposed to automatically denoise the noisy ECG signal. The ECG signals used are from clinical data marked by expert diagnostics. In addition, we also compared several popular denoising methods proposed in the past to conduct comparative experiments to verify the denoising performance of the model. The experimental results show that the proposed U-net network based on SRM channel attention can remove the noise contained in the ECG signal while retaining the characteristic shape of the ECG signal, and has a good effect in terms of signal-to-noise ratio and root mean square error.

Approximately 50% of Alzheimer's disease (AD) patients will develop psychotic symptoms and these patients will experience severe rapid cognitive decline compared with those without psychosis (AD-P). Currently, no medication has been approved by the Food and Drug Administration for AD with psychosis (AD+P) specifically, although atypical antipsychotics are widely used in clinical practice. These drugs have demonstrated modest efficacy in managing psychosis in individuals with AD, with an increased frequency of adverse events, including excess mortality. We compared the differences between the genetic variations/genes associated with AD+P and schizophrenia from existing Genome-Wide Association Study and differentially expressed genes (DEGs). We also constructed disease-specific protein-protein interaction networks for AD+P and schizophrenia. Network efficiency was then calculated to characterize the topological structures of these two networks. The efficiency of antipsychotics in these two networks was calculated. A weight adjustment based on binding affinity to drug targets was later applied to refine our results, and 2013 and 2123 genes were identified as related to AD+P and schizophrenia, respectively, with only 115 genes shared. Antipsychotics showed a significantly lower efficiency in the AD+P network than in the schizophrenia network (P < 0.001) indicating that antipsychotics may have less impact in AD+P than in schizophrenia. AD+P may be caused by mechanisms distinct from those in schizophrenia which result in a decreased efficacy of antipsychotics in AD+P. In addition, the network analysis methods provided quantitative explanations of the lower efficacy of antipsychotics in AD+P.

The detection of consecutive cardiac cycles plays an important role in the daily monitoring of cardiovascular diseases. The Seismocardiogram (SCG) signal measures the cardiac-induced vibrations and is suitable for continuously tracking since it can be measured in a non-invasive fashion. In this paper, a beat-to-beat heart rate detection method based on low-frequency SCG signal using BiLSTM network is proposed. The regression model to predict Electrocardiogram (ECG) from SCG was trained on CEBS dataset and a 5-fold cross-validation method was adopted. The RMSE of the predicted ECG signal and ECG signal was 0.0367. The sensitivity and precision achieved 0.97 and 0.98 separately and the Spearman Correlation was 0.98. This work shows promise for the proposed SCG based methodology to be used as an alternative to ECG for continuous beat-to-beat heart rate monitoring.

Scanning electron microscope image acquisition system is designed to control the scanning generator, collect the real-time image of the scanning electron microscope system, and process the image to help users get high quality and resolution sample images. Electronic microscope image acquisition system software which adapts Client/Server software architecture and develops based on the WPF framework, is aimed at controlling the scanning electron microscope imaging system. After further system testing, the result indicates that this system not only can realize the functions of image display in real-time, image measurement and annotation, image restoration and image storage, but also can attain high quality images by image restoration algorithms, which can meet automated and diversified requirements of the scanning electron microscope operators to capture electron microscope images.

Electrocardiogram (ECG) signal is a common diagnostic basis for heart disease. Reliable waveform detection method for ECG has important applications in clinical diagnosis. This work proposes a new method for P- and T- wave detection based on auxiliary waveform. The algorithm includes three parts: preprocessing, construction of auxiliary waveforms and P- and T-wave position detection. First, the ECG signal is preprocessed to remove baseline drift and high-frequency noise. Then, the auxiliary waveforms is constructed in the P- and T-wave searching windows which are defined based on the QRS wave position. Next, on the basis of the prominence of the auxiliary waveform, each lead of the 12-lead ECG signal is weighted and the final auxiliary waveform is constructed. Finally the P/T-wave positions are detected according to the auxiliary waveform memory. The performance of the proposed algorithm has been evaluated on a 12-lead manually annotated database. Standard deviations of 26.55 and 30.92 ms were obtained for the onset and offset of T-wave respectively, the standard deviation of the onset and offset of P-wave is 13.25 and 15.7 ms. The algorithm we proposed can provide reference for following research.

Atrial fibrillation and atrial flutter are two more common rapid atrial arrhythmias, they are independent of each other and can transform each other. Because the clinical characteristics of the two diseases are similar, it is easy to cause misdiagnosis. Therefore, effective diagnosis of distinguishing atrial fibrillation and atrial flutter can reduce the damage suffered by patients during treatment. In this paper, we propose a kind of based on residual network and multi-scale feature fusion structure of neural network to automatically classify atrial fibrillation, atrial flutter and other ECG signals. The ECG signals used are derived from clinical data labeled by expert diagnosis. In addition, we used several popular classification methods proposed in the past for comparative experiments to verify the classification performance of our models. The experimental results show that the total F1, recall and precision of the proposed algorithm are 0.923, 0.916 and 0.93. The algorithm we propose has the potential to provide reference for subsequent research.

Electrocardiogram (ECG), as an important method for diagnosing cardiovascular diseases, can record the heart activity over a period of time. However, most of the current studies on ECG classification focus on the single scale information and ignore the complementary information between different scales. Therefore, this paper proposed an end-to-end multi-scale fusion convolutional neural network (CNN) for heartbeat classification. In this method, multiple convolution kernels of different reception domains are used to extract unique features of different scales, and the extracted multiple scale features are fused, which could effectively capture disease patterns and suppress noise interference. At the same time, attention module is used to select features to improve model performance. Improve efficiency with residual module. Finally, we obtained 34, 983 heartbeats from the Physikalisch-Technische Bundesanstalt (PTB) dataset to validate the model performance. The overall Fl-score is 99. 69%, and the Fl-score of each single class is more than 99. 35%, which is better than the existing algorithms. It can be described as a reference for future research.

Wearable electrocardiogram (ECG) monitoring systems were effective ways to diagnose intermittent cardio diseases. However, the Electrode Motion Artifact (EMA) and the Muscle Artifact (MA) destroy the incipient shape of ECG signals, decrease the accuracy of diagnostic results. Here, we proposed a novel method for detecting destroyed segments of ECG signals. The method was based on a deep learning network, and its performance was evaluated on a synthetic dataset of the MIT-BIH arrhythmia database. Its practicability was tested with three R-peak detection algorithms. By removing the destroyed segments in ECG signals, the sensitive and positive prediction of these R-peak detection algorithms were promoted significantly.

Atrial fibrillation is a serious cardiovascular disease. It is the main cause of heart disease such as myocardial infarction. ECG based atrial fibrillation detection is very important for clinical diagnosis. In this paper, a method based on one-dimensional CNN and time domain features of ECG sequence is proposed to detect atrial fibrillation. The ECG data used came from the MIT-BIH atrial fibrillation database. The first step is to filter out the noise interference in ECG. In the second step, ECG signals were segmented into seven heart beats. In the third step, 8 features are extracted based on the time domain features of ECG sequence to form the feature vector (size 1*8). In the fourth step, the one-hot label (1*2) output by the convolutional neural network was combined with the extracted time domain features (size 1*8) to obtain a total of 10 dimensional features. In the fifth step, the extracted 10-dimensional features are normalized and then put into the SVM classifier. The experimental results show that the sensitivity, specificity and total accuracy of the proposed algorithm are 99.07%, 97.05% and 98.03%, respectively. This algorithm has great potential to help doctors and reduce mortality.

The automatic segmentation of esophageal tissue layers in OCT images is essential for the study of esophageal diseases and computer-aided diagnosis. The tissue layer thickness of the esophagus is an important diagnostic sign for many esophageal diseases. Manually marking the boundary to calculate the average thickness of each layer is time-consuming and susceptible to subjective factors of the marker. In this paper, we propose a Pyramid Pooling Channel Attention Network (PPCANet) for the tissue segmentation. On the basis of the PSPNet, a channel attention module is introduced to selectively emphasize interdependent channel maps by integrating associated features among all channel maps, which makes the segmentation more precise. The potential clinical application of PPCANet for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in this paper.

With the popularization of computer technology and the development of signal processing technology, the computer automatic diagnosis of ECG has entered the practical stage. In this paper, a multi-functional ECG analysis software system is designed and implemented, which can read and analyze ECG signals and draw ECG waveforms, calculate ECG parameters such as heart rate (HR), P wave time and RR interval, and draw corresponding diagnostic conclusions. C# was used as programming language in Windows and WPF framework for software development. Software design based on model-View-Controller (MVC) framework; Access ECG data to disk through file IO operation; the ECG data of 40 patients were processed and analyzed, and the results were compared with doctors' diagnosis results. After testing, the software system can run stably on PC. The accuracy of the calculation of the relevant parameters of 40 sets of data is above 95%, and the accuracy of the interpretation of normal ECG is above 98%. The accuracy rate of arrhythmia detection is over 90%. This ECG analysis software system has certain clinical application value.

Many researches have been carried out to explore the ultrastructure of organisms at home and abroad, and different algorithms have been used to reconstruct the images of different samples at the nanoscale. Image “matching” and “fusion” are two significant research fields that directly affect the performance of image Mosaic. As image stitching the first and the last step, if there is no correct image matching and fusion algorithm, it is almost impossible to successful image stitching. Image registration is to map one image to another by looking for a space transformation for two images in a set of image data, so that the points corresponding to the same position in the space in the two images correspond one to one, so as to achieve the purpose of information fusion. In this paper, the feature-based image stitching algorithm was used to transform the analysis of the whole image into the analysis of some features of the image, which greatly reduced the calculation amount. Meanwhile, the feature points were further optimized and selected to reduce the mismatching rate, and the electron microscope images of autistic mice were mosaically splicing. In the registration part, this paper adopts the elastic registration method and makes fine adjustments on this basis to obtain more accurate registration results, which will be helpful for the subsequent segmentation of each organizational structure.

In this work, arrhythmia appearing with the presence of abnormal heart electrical activity is efficiently recognized and classified. A novel method is proposed for accurate recognition and classification of cardiac arrhythmias. Firstly, P-QRS-T waves is segmented from ECG waveform; secondly, morphological features are extracted from P-QRS-T waves, and ECG segment features are extracted from the selected ECG segment by using PCA and dynamic time warping(DTW); finally, SVM is applied to the features and automatic diagnosis results is presented. ECG data set used is derived from the MIT-BIH in which ECG signals are divided into the four classes: normal beats(N), supraventricular ectopic beats (SVEBs), ventricular ectopic beats (VEBs) and fusion of ventricular and normal (F). Our proposed method can distinguish N, SVEBs, VEBs and F with an accuracy of 97.80 percent. The sensitivities for the classes N, SVEBs, VEBs and F are 99.27, 87.47, 94.71, and 73.88 percent and the positive predictivities are 98.48, 95.25, 95.22 and 86.09 percent respectively. The detection sensitivity of SVEBs and VEBs has a better performance by combining proposed features than by using the ECG morphology or ECG segment features separately. The proposed method is compared with four selected peer algorithms and delivers solid results.

In the above-named work, the spelling of the second author’s name was incorrect. The correct spelling is given.

Accurate lung tumor delineation plays an important role in radiotherapy treatment planning. Since the lung tumor has poor boundary in positron emission tomography (PET) images and low contrast in computed tomography (CT) images, segmentation of tumor in the PET and CT images is a challenging task. In this paper, we effectively integrate the two modalities by making fully use of the superior contrast of PET images and superior spatial resolution of CT images. Random walk and graph cut method is integrated to solve the segmentation problem, in which random walk is utilized as an initialization tool to provide object seeds for graph cut segmentation on the PET and CT images. The co-segmentation problem is formulated as an energy minimization problem which is solved by max-flow/min-cut method. A graph, including two sub-graphs and a special link, is constructed, in which one sub-graph is for the PET and another is for CT, and the special link encodes a context term which penalizes the difference of the tumor segmentation on the two modalities. To fully utilize the characteristics of PET and CT images, a novel energy representation is devised. For the PET, a downhill cost and a 3D derivative cost are proposed. For the CT, a shape penalty cost is integrated into the energy function which helps to constrain the tumor region during the segmentation. We validate our algorithm on a data set which consists of 18 PET-CT images. The experimental results indicate that the proposed method is superior to the graph cut method solely using the PET or CT is more accurate compared with the random walk method, random walk co-segmentation method, and non-improved graph cut method.

This paper describes an automatic scoring system for answer sheet of blind students, It is completed of the template bags for blind students, the machine-readable answer sheet, the cursor reader and the lines of transmiss datas and the computers is equipped with scoring analysis module, inside: answer sheet attached to the template bags, Braille scoring module controls the OMR to collect the data of answer sheet, and transported the information to the computer, at last the computer recognized the braille answer sheet by scoring load module and output the score.

Inverted pendulum is a nonlinear, multivariable and instability system. In the past, without considering the effects of measurement noise and output noise of the sensor in the processing of inverted pendulum control, the strong random jitter occur, and the control accuracy and stable of the inverted pendulum is reduced. Because the Kalman filter can estimate the signal is corrupted by the noise, the filtered system will have fine robustness and dynamic performance. Inverted pendulum in the straight line car is given as example, and the application of Kalman filter in the inverted pendulum control system is described in the paper.

In this paper, through the study of the characteristic rule of rolling massage experts' forearm surface EMG signal (sEMG) in rolling massage, the rolling massage analysis and evaluation methods based on the analysis of sEMG is presented. Therefore, during the implementation of rolling massage, muscle semg signals of the massage's three block muscle was collected at the real time, the relations between the electromyographic signal changeable characteristics and rolling method massage strength was discussed, meanwhile massager's muscle force situation was monitored, the ratio of muscle force was analyzed to assess and correct the massage action for the person whose action is not standard.

Learning algorithms of the support vector machine is to map the input vector to a high dimensional space through certain kernel function and separate the image of the original linear input vector with the maximum of interval under consideration. This paper is about the limb motion recognition problem of stroke patients, mapping the input vector to the reproducing kernel RKHS (reproducing Kernel Hilbert space) space and using the methods in linear space to solve nonlinear problems. Meanwhile, feature transformation is achieved by defining the inner product of samples in the feature space after its characteristics are changed. Experimental results show that the support vector machine which is made up of new Kernel function can greatly improve the recognition rate of action under the conditions of Mercer, providing theoretical basis for modeling of lower limb rehabilitation training system of stroke patients.

Launched from the establishment of 3D tongue model in this thesis, the tongue rehabilitation training method of hearing-impaired child combined by 3D modeling technology and sensor technology is applied and the method can reveal the tongue motor process of average person when pronouncing, guide the hearing-impaired child to do tongue pronunciation training, overcome the problem of non-visibility of tongue training process in speech training of hearing-impaired child, evaluate rehabilitation training degree and cover the shortage of no feedback diagnosis in the rehabilitation training of hearing-impaired so as to enhance the efficiency of hearing-impaired child pronunciation training and accuracy of pronunciation training.

One method was proposed for recovering structure model of the object by using monocular vision equipment. The fundamental idea can be summed up as sequential reconstruction under hierarchical method. First of all, by dividing the single observation path into multiple, more details of structure can be observed. After outliers removing, these sequences can be aligned with the referenced one which can be specified by users. Finally, after surface fitting, depth map of the object can be densely retrieved. The experiment shows that the depth map can be retrieved much densely than traditional method from the same dataset.

This paper describe a feasible scheme of local visual navigation, local visual navigation application scenarios often some with complex background, target species more real scenario that the obstacle avoidance is particularly important. In particular the visual navigation target segmentation in the background and the foreground objects more complex scenarios important for predicting pre-step. This visual navigation closer to the qualitative analysis of the scene, so the performance can be weak, but easy to implement, without artificial markers, but more dependent on hardware performance, available online AP Cluster is running.

Autism is a disorder of neural development characterized by impaired social interaction and verbal and non-verbal communication, and by restricted, repetitive or stereotyped behavior. Visual supports can alleviate these challenges by juxtaposing communication with visual cues. In this paper, a "Rehabilitation Training System (RTS)" for children with autism is developed. The "RTS" is a computer based training system that can be used for training children with autism to learn basic shapes, colors, plants, animals, and find the odd man out etc., from a given set of patterns. It not only eases the teacher's effort but also motivates the child to learn the patterns by providing incentives via audio signals.

Breath training is an important part in the pronunciation recovery process, we simulate the fluent of air in the the upper respiratory tract, use computational fluid dynamic to build the impaired hearing children's pronunciation recovery system, and we have tasted the reliability of the system by experimental. We study the two parts in the breath process by the dynamic fluid computational method, and build the 3D model for the breath process. The results can be used to direct the pronunciation training for impaired hearing children. One can evaluate the trainer's breath process according the model, to find the weaking part in the process, to direct it. It will be useful in the pronunciation recovery process.

This paper investigates geometric feature extraction from scanned image and applies it in multi-view image registration. The presented registration approach includes three steps, feature extraction, coarse registration and fine registration. Firstly, feature points are identified based on curvature estimation, and feature point linkage is set up according to neighboring relationship of the extracted feature points. The coarse registration is conducted by alignment transmission calculation using the overlapping feature linkages extracted from the two-view images. Finally, iterative closest point (ICP) is used in fine registration. Experimental results of multi-view images taken by laser scanner are carried out to compare the convergence and registration error between the presented approaches with classical ICP. The presented registration approach achieves higher convergence than classical ICP, and can overcome the problems of traditional ICP in low overlapping and bad initial estimate.

Large-volume 3D triangle mesh model has large difficult to render, store, and transmit in Internet. This research investigates a 3D (3-dimension) streaming technology to overcome the difficulties in model transmission over Web. Edge collapse based mesh simplification and progressive mesh refinement are studied, which is crucial to establish 3D streaming technology. A prototype with GUI for mesh simplification and refinement and peer-to-peer network architecture are developed to implement the 3D streaming technology for Internet-enabled transmission of 3D mesh model.

BACKGROUND: As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated. RESULTS: In this work, we present a novel, versatile and comprehensive program, PPSP (Prediction of PK-specific Phosphorylation site), deployed with approach of Bayesian decision theory (BDT). PPSP could predict the potential phosphorylation sites accurately for approximately 70 PK (Protein Kinase) groups. Compared with four existing tools Scansite, NetPhosK, KinasePhos and GPS, PPSP is more accurate and powerful than these tools. Moreover, PPSP also provides the prediction for many novel PKs, say, TRK, mTOR, SyK and MET/RON, etc. The accuracy of these novel PKs are also satisfying. CONCLUSION: Taken together, we propose that PPSP could be a potentially powerful tool for the experimentalists who are focusing on phosphorylation substrates with their PK-specific sites identification. Moreover, the BDT strategy could also be a ubiquitous approach for PTMs, such as sumoylation and ubiquitination, etc.