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Patch-based medical image registration has been well explored in recent decades. However, the patch fusion process can generate grid-like artifacts along the edge of patches for the following two reasons: firstly, in order to ensure the same size of input and output, zero-padding is used, which causes uncertainty in the edges of the output feature map during the feature extraction process; secondly, the sliding window extraction patch with different strides will result in different degrees of grid-like artifacts. In this paper, we propose an exponential-distance-weighted (EDW) method to remove grid-like artifacts. To consider the uncertainty of predictions near patch edges, we used an exponential function to convert the distance from the point in the overlapping regions to the center point of the patch into a weighting coefficient. This gave lower weights to areas near the patch edges, to decrease the uncertainty predictions. Finally, the dense displacement field was obtained by this EDW weighting method. We used the OASIS-3 dataset to evaluate the performance of our method. The experimental results show that the proposed EDW patch fusion method removed grid-like artifacts and improved the dice similarity coefficient superior to those of several state-of-the-art methods. The proposed fusion method can be used together with any patch-based registration model.

Background and Objective: Recent advances in deep learning have been applied to ECG detection and obtained great success. The spatial and temporal information from ECG signals is fused by combining convolutional neural networks (CNN) with recurrent neural network (RNN). However, these networks ignore the different contribution of local and global segments of a feature map extracted from the ECG and the correlation relationship between the above two segments. To address this issue, a novel convolutional neural network with non-local convolutional block attention module(NCBAM) is proposed to automatically classify ECG heartbeats., Methods: Our proposed method consists of a 33-layer CNN architecture followed by a NCBAM module. Initially, preprocessed electrocardiogram (ECG) signals are fed into the CNN architecture to extract the spatial and channel features. Further, long-range dependencies of representative features along spatial and channel axis are captured by non-local attention. Finally, the spatial, channel and temporal information of ECG are fused by a learned matrix. The learned matrix is to mine rich relationship information across the above three types of information to make up for the different contribution., Results and Conclusion: The proposed method achieves an average F1 score of 0.9664 on MIT-BIH arrhythmia database, as well as AUC of 0.9314 and F max of 0.8507 on PTB-XL ECG database. Compared with the state-of-the-art attention mechanism based on the same public database, NCBAM achieves an obvious improvement in classifying ECG heartbeats. The results demonstrate the proposed method is reliable and efficient for ECG beat classification., Competing Interests: Declaration of Competing Interest There is no conflict of interest in this work., (Copyright © 2021 Elsevier B.V. All rights reserved.)

A Poincaré plot is a return map that geometrically elucidates the progression of a time-series. It has frequently been used in heart rate variability analyses. However, algorithms for dedicatedly dissecting the shape of this geometrical plot are yet to be established. In this study, we proposed a gridded Poincaré plot by coarse-graining the original graph and using the newly proposed one, defined two novel measures, namely gridded distribution rate (GDR) and gridded distribution entropy (GDE). The GDR essentially represents the percentage of grids with points, while the GDE estimates the Shannon entropy of the grid weight; that is, the number of points in each grid. The performances of the two measures were examined using both theoretical data with known dynamics and experimental short-term RR interval time-series, and they were compared with several existing metrics. Simulation tests demonstrated that both the GDR and GDE could distinguish among different dynamics, while all the compared methods failed. The experimental results further indicated the ability of the GDR and GDE to differentiate healthy young people from healthy aged adults as well as distinguish healthy subjects from patients with coronary artery disease. Our results suggest that the proposed GDR and GDE may better characterize the Poincaré plot in terms of differentiating between varying dynamical regimes, and between human physiological or pathological conditions. Further studies are warranted to establish their feasibility in evaluating cardiovascular functions in clinical practice., (Copyright © 2019. Published by Elsevier Ltd.)

Objective: In this study, we developed a joint distribution entropy (JDistEn) method to robustly estimate the coupling in short physiological series., Methods: The JDistEn method is derived from a joint distance matrix which is constructed from a combination of the distance matrix corresponding to each individual data channel using a geometric mean calculation. A coupled Rössler system and a coupled dual-kinetics neural mass model were used to examine how well JDistEn performed, specifically, its sensitivity for detecting weak coupling, its consistency in gauging coupling strength, and its reliability in processing input of decreased data length. Performance of JDistEn in estimating physiological coupling was further examined with bivariate electroencephalography data from rats and RR interval and diastolic time interval series from human beings. Cross-sample entropy (XSampEn), cross-conditional entropy (XCE), and Shannon entropy of diagonal lines in the joint recurrence plots (JENT) were applied for purposes of comparison., Results: Simulation results suggest that JDistEn showed markedly higher sensitivity than XSampEn, XCE, and JENT for dynamics in weak coupling, although as the simulation models were more intensively coupled, JDistEn performance was comparable to the three others. In addition, this improved sensitivity was much more pronounced for short datasets. Experimental results further confirmed that JDistEn outperformed XSampEn, XCE, and JENT for detecting weak coupling, especially for short physiological data., Conclusion: This study suggested that our proposed JDistEn could be useful for continuous and even real-time coupling analysis for physiological signals in clinical practice.

Background: Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term variability measurement (e.g., 5 min) has drawn increasing attention in clinical practice, since it is able to provide almost immediate measurement results and enables the real-time monitoring of cardiovascular function. However, it is still a contemporary challenge to robustly estimate the HRV and DPV parameters based on short-term recordings., Methods: In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting a piecewise fuzzy membership function for the Gaussian function in conventional fuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise compared with sample entropy (SampEn) and FuzzyEn, were examined by two well-accepted simulation models-the [Formula: see text] noise and the Logistic attractor. The rFuzzyEn was further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients with coronary artery stenosis and healthy volunteers., Results: Simulation results showed smaller fluctuations in the rFuzzyEn than in SampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance. Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group. However, no or less significant difference was observed between the two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05)., Conclusions: Our proposed rFuzzyEn outperformed conventional SampEn and FuzzyEn in terms of both stability and robustness against additive noise, particularly when the data set was relatively short. Analysis of DPV using rFuzzyEn may provide more valuable information to assess the cardiovascular states than the other entropy measures and has a potential for clinical application.

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Mastoidectomy is a core surgical procedure in otologic surgery. It is believed that the procedure is performed by different surgeons with some variability. However, it is also believed that all surgeons use a finite number of fundamental surgical actions to complete the procedure. To determine how a surgeon performs a mastoidectomy, we sought to identify the fundamental surgical actions [called action primitives (APs)] and determine the transition boundaries among those APs. Our motivation for this paper is both to delineate the APs necessary to complete a mastoidectomy and to optimize and potentially automate the surgical process. In this paper, we present a new approach to developing methods for parsing raw data (position and orientation of the surgical tool and end-effector force) into a sequence of surgical tasks. The overall objective is to de-construct the surgical procedure into a series of APs. This paper presents results from our initial investigation on detecting transition boundaries and identifying APs involved in mastoidectomy.

Shortening the lead time for Product Development (PD) provides enterprises with a competitive advantage. Given the iterative nature of PD projects, two aspects are regularly considered to shorten the PD lead time, that is, conducting faster or fewer iterations. However, executing faster iterations usually causes more iterations and vice versa. Therefore, suitable coordination between faster and fewer iterations is necessary to minimize the PD lead time. We investigate this coordination from a strategic perspective, whereby a PD project is considered as a sequence of stages and characterized by the design rates and rework probabilities of those stages. We model the coordination as a decision to choose the appropriate design rates for each stage, wherein the rework probabilities are negatively related to the design rates. An absorbing Markov process is applied to calculate the expected lead time of a PD project. Further, we formulate a geometric programming model to determine the optimal design rates of the stages with respect to the minimal expected lead time. Several insights are extracted from the model to provide general guidance on the coordination, including the effect of the acceptance check rate of the project, rework risk of the stages on the optimal design rates, and decomposability of the coordination. Inspired by these insights, an efficient heuristic algorithm is designed. The algorithm performs well in numerical experiments, which in turn validates the insights. Additionally, a field case proves the effectiveness of our model. Compared with the current policy, 12.25% of the PD lead time is saved through appropriate coordination between faster and fewer iterations. [ABSTRACT FROM AUTHOR]

Objective: To evaluate noninvasively the severity of esophageal varices (EV) in cirrhotic patients using splenic hemodynamics obtained with dual-energy CT.Methods: We retrospectively analyzed 72 cirrhotic patients with EV between December 2018 and June 2019. Patients were divided into three groups: mild (EV1), medium (EV2), or severe (EV3) EV groups based on severity of EV assessed by endoscopy. An additional control group included 20 patients with normal liver CT. All patients underwent contrast-enhanced dual-energy CT. The iodine weight in spleen (IW-S) was calculated as IW-S = IC-S (iodine concentration in spleen) × V-S (spleen volume). Differences between EV and control groups were analyzed using one-way analysis of variance with Welch's correction. Games-Howell test made further pairwise comparison. The diagnostic value of IW-S on high-risk EV (EV2, EV3, or EV1 with red color sign) was evaluated using the ROC curve. p < 0.05 indicated statistical significance.Results: The overall difference of IW-S between the control and EV groups was statistically significant (p < 0.001). Patients with more severe EV had higher IW-S values. Pairwise comparisons showed that except for control vs. EV1 groups, the IW-S between any other two groups was significantly different (p < 0.05). With a cutoff value at 1087 mg, the AUC for using IW-S for the detection of high-risk EV was 0.87 (95% CI 0.77~0.94). Sensitivity and specificity were 84.9% and 84.2%, respectively.Conclusion: IW-S obtained with dual-energy CT can noninvasively predict EV severity.Key Points: • A higher iodine weight in spleen (IW-S) was observed in case of severe esophageal varices. • Cirrhotic patients have significantly higher IW-S than normal-liver patients. • IW-S in dual-energy CT maybe used to evaluate the severity of EV. [ABSTRACT FROM AUTHOR]

This article considers a resource-constrained project scheduling problem with a single shared resource. In this model, multiple processors are required to complete jobs with a certain amount of shared resource. The supply of the resource is limited and must be shared between processors. A column-generation-based algorithm with some enhancement techniques, including stabilization, a mechanism to update the solution pool, and an approximate solution technique, is proposed. Finally, extensive computational experiments are conducted to evaluate the performance of the proposed method by comparing it with Lagrangian relaxation, CPLEX ® and a self-adapting genetic algorithm. The results prove the proposed method has an advantage in terms of the objective and CPU time. Numerical experiments are also conducted to verify the effectiveness of the proposed enhancements. [ABSTRACT FROM AUTHOR]

Entropy measures that assess signals’ complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods—fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)–were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices. [ABSTRACT FROM AUTHOR]

This study examines the berth allocation problem with the consideration of uncertainty factors, including the arrival and operation times of the calling vessels. A bi-objective robust berth allocation model, which focuses on economic performance and customer satisfaction, is formulated. The model aims to optimize the robustness of the berth allocation policy, and an adaptive grey wolf optimizer algorithm is developed to solve the proposed model. The performance of the heuristic is evaluated through randomly generated instances. Experimental results show that the proposed heuristic provides good solution quality and calculation efficiency. [ABSTRACT FROM AUTHOR]

The baPWV measurement is a non-invasive and convenient technique in an assessment of arterial stiffness. Despite its widespread application, the influence of different timing algorithms is still unclear. The present study was conducted to investigate the influence of six timing algorithms (MIN, MAX, D1, D2, MDP and INS) on the baPWV measurement and to evaluate the performance of them. Forty-five CAD patients and fifty-five healthy subjects were recruited in this study. A PVR acquisition apparatus was built up for baPWV measurement. The baPWV and other related parameters were calculated separately by the six timing algorithms. The influence and performance of the six algorithms was analyzed. The six timing algorithms generate significantly different baPWV values (left: F=29.036, P<0.001; right: F=40.076, P<0.001). In terms of reproducibility, the MAX has significantly higher CV value (&nvge; 18.6%) than the other methods, while the INS has the lowest CV value (≤ 2.7%). On the performance of classification, the INS produces the highest AUC values (left: 0.854; right: 0.872). The MIN and D2 also have a passable performance (AUC > 0.8). The choice of timing algorithm affects baPWV values and the quality of measurement. The INS method is recommended for baPWV measurement. [ABSTRACT FROM AUTHOR]

The baPWV measurement is a non-invasive and convenient technique in an assessment of arterial stiffness. Despite its widespread application, the influence of different timing algorithms is still unclear. The present study was conducted to investigate the influence of six timing algorithms (MIN, MAX, D1, D2, MDP and INS) on the baPWV measurement and to evaluate the performance of them. Forty-five CAD patients and fifty-five healthy subjects were recruited in this study. A PVR acquisition apparatus was built up for baPWV measurement. The baPWV and other related parameters were calculated separately by the six timing algorithms. The influence and performance of the six algorithms was analyzed. The six timing algorithms generate significantly different baPWV values (left: F=29.036, P<0.001; right: F=40.076, P<0.001). In terms of reproducibility, the MAX has significantly higher CV value (&nvge; 18.6%) than the other methods, while the INS has the lowest CV value (≤ 2.7%). On the performance of classification, the INS produces the highest AUC values (left: 0.854; right: 0.872). The MIN and D2 also have a passable performance (AUC > 0.8). The choice of timing algorithm affects baPWV values and the quality of measurement. The INS method is recommended for baPWV measurement. [ABSTRACT FROM AUTHOR]

Product configuration technology is the core of the modular design of computer numerical control (CNC) machine tools. Combined with modular design technology and case-based reasoning (CBR) technology, this paper proposed a modular configuration design method based on CBR for CNC machine tools. Using the fuzzy similarity priority ratio and gray correlation analysis algorithm, respectively, the calculation of case priority and the case evaluation are realized and the best match case of the user needs is selected. The module configuration system was developed using MATLAB/gui. By designing the example and analyzing the result, the configuration system is more rapid and accurate than conventional methods in the process to find the best match instance. [ABSTRACT FROM AUTHOR]

• Acoustic time difference algorithm can reduce error of acoustic time difference. • Acoustic time difference algorithm can improve bolt stress detection accuracy. • Kalman nonlinear filter is constructed to select initial values and observe noise. • Filter and cross-correlation algorithm can estimate the acoustic time difference. Ultrasonic measurement is an effective technical method for the bolt stress detection of the precise structural connection. This paper proposed an acoustic time difference algorithm based on adaptive hybrid extended Kalman filter to reduce the calculation error of acoustic time difference in bolt stress ultrasonic measurement. The initial value is firstly selected by particle filter algorithm, and the noise variance is measured by the Sage-Husa estimator. Then, a Kalman nonlinear filter is constructed to adaptively select initial values and observe noise. Thirdly, the filter and cross-correlation algorithm are used to accurately estimate the acoustic time difference. Finally, the signal simulation method and the ultrasonic measurement platform of bolt axial stress are built, and the proposed algorithm and the traditional algorithm are respectively used to calibrate and measure the correspondence between acoustic time difference and axial stress of the M8 coarse tooth half-thread bolt. The bolt tightening experimental and simulation results showed that the processed signal algorithm is verified and can effectively improve the measurement accuracy of bolt axial stress. [ABSTRACT FROM AUTHOR]