Non-invasive continuous blood pressure prediction based on ECG and PPG fusion map.

To achieve real-time blood pressure monitoring, a novel non-invasive method is proposed in this article. Electrocardiographic (ECG) and pulse wave signals (PPG) are fused from a multi-omics signal-level perspective. A physiological signal fusion matrix and fusion map, which can estimate the blood pressure of blood loss(BPBL), are constructed. The results demonstrate the efficacy of the fusion map model, with correlation values of 0.988 and 0.991 between the estimated BPBL and the true systolic blood pressure (SBP) and diastolic blood pressure (DBP), respectively. The root mean square errors for SBP and DBP were 3.21 mmHg and 3.00 mmHg, respectively. The model validation showed that the fusion map method is capable of simultaneous highlighting of the respective characteristics of ECG and PPG and their correlation, improving the utilization of the information and the accuracy of BPBL. This article validates that physiological signal fusion map can effectively improve the accuracy of BPBL estimation and provides a new perspective for the field of physiological information fusion.

Time fused coefficient SIR model with application to COVID-19 epidemic in the United States.

In this paper, we propose a Susceptible-Infected-Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR model's transmission rate and removal rate via Bayesian shrinkage priors. MCMC sampling for the proposed method is facilitated by the nimble package in R. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. We further apply the proposed methodology to analyze different levels of COVID-19 data in the United States.

Peripheral intravenous infusion for children based on computer digital video technology.

PURPOSE: Intravenous infusion techniques often take a long time and require continuous monitoring of the infusion, and the needle must be removed promptly at the end of the injection, placing a burden on the patient, family and medical workers. Thus, this paper presents a droplet flow rate monitoring system based on an embedded system. METHODS: In our retrospective study, according to different intervention measures, 80 patients were divided into two groups to compare the improvement of children's adverse psychological conditions. The patients were divided into control and intervention groups, with 40 patients in each group. Patients in the intervention group received psychological care and Online Real-Time Fusion Method to monitor the infusion state. Patients in the control group received routine infusions. Computer digital video technology and online real-time fusion algorithm were utilized to determine whether the parameters of the intravenous infusion model were optimized. The model's accuracy was used to evaluate whether the success rate of one-time needle insertion was improved. RESULTS: The system can also be used for other monitoring. It is simple and convenient to set up and dismantle. The system can significantly relieve medical workers and patients. It was found that the observation group is significantly better than the control group in terms of psychological improvement (P<0.05). Comparing the patient's satisfaction, the difference between the two groups was statistically significant. CONCLUSION: When administering intravenous infusions in children, applying various forms of psychological interventions and giving emotional support can significantly improve their compliance and their mental state. It is of great significance to enhance the effect of infusions in patients.

A New Model of Interval-Valued Intuitionistic Fuzzy Weighted Operators and Their Application in Dynamic Fusion Target Threat Assessment.

Existing missile defense target threat assessment methods ignore the target timing and battlefield changes, leading to low assessment accuracy. In order to overcome this problem, a dynamic multi-time fusion target threat assessment method is proposed. In this method, a new interval valued intuitionistic fuzzy weighted averaging operator is proposed to effectively aggregate multi-source uncertain information; an interval-valued intuitionistic fuzzy entropy based on a cosine function (IVIFECF) is designed to determine the target attribute weight; an improved interval-valued intuitionistic fuzzy number distance measurement model is constructed to improve the discrimination of assessment results. Specifically, first of all, we define new interval-valued intuitionistic fuzzy operation rules based on algebraic operations. We use these rules to provide a new model of interval-valued intuitionistic fuzzy weighted arithmetic averaging (IVIFWAA) and geometric averaging (IVIFWGA) operators, and prove a number of algebraic properties of these operators. Then, considering the subjective and objective weights of the incoming target, a comprehensive weight model of target attributes based on IVIFECF is proposed, and the Poisson distribution method is used to solve the time series weights to process multi-time situation information. On this basis, the IVIFWAA and IVIFWGA operators are used to aggregate the decision information from multiple times and multiple decision makers. Finally, based on the improved TOPSIS method, the interval-valued intuitionistic fuzzy numbers are ordered, and the weighted multi-time fusion target threat assessment result is obtained. Simulation results of comparison show that the proposed method can effectively improve the reliability and accuracy of target threat assessment in missile defense.

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features.

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

Real-time US-CT/MR fusion imaging for percutaneous radiofrequency ablation of hepatocellular carcinoma.

BACKGROUND & AIMS: Although ultrasonography (US) guided radiofrequency ablation (RFA) is a commonly used treatment option for early hepatocellular carcinoma (HCC), inconspicuous tumors on US limits its feasibility. Thus, we prospectively determined whether real-time US-CT/MR fusion imaging can improve the technical feasibility of RFA compared with B-mode US, and help predict local tumor progression after RFA in patients with HCC. METHODS: A total of 216 patients with 243 HCCs ⩽5cm referred for RFA were prospectively enrolled. Prior to RFA, the operators scored the visibility of tumors, and technical feasibility on a 4-point scale at both B-mode US and fusion imaging. RFA was performed with a switching monopolar system using a separable cluster electrode under fusion imaging guidance. Technique effectiveness, local tumor progression and intrahepatic remote recurrences were evaluated. RESULTS: Tumor visibility and technical feasibility were significantly improved with fusion imaging compared with B-mode US (p<0.001). Under fusion imaging guidance, the technique effectiveness of RFA for invisible tumors on B-mode US was similar to those for visible tumors (96.1% vs. 97.6%, p=0.295). Estimated cumulative incidence of local tumor progression at 24months was 4.7%, and previous treatment for other hepatic tumors (p=0.01), higher expected number of electrode insertions needed and lower technical feasibility scores (p<0.01) on fusion imaging were significant negative predictive factors for local tumor progression. CONCLUSION: Real-time fusion imaging guidance significantly improved the tumor visibility and technical feasibility of RFA in patients with HCCs compared with B-mode US, and low feasibility scores on fusion imaging was a significant negative predictive factor for local tumor progression. LAY SUMMARY: US/CT-MR fusion imaging guidance improved the tumor visibility and technical feasibility of RFA in patients with HCCs. In addition, fusion imaging guided RFA using multiple electrodes demonstrated a high technique effectiveness rate and a low local tumor progression rate during mid-term follow-up. Clinical trial number: ClinicalTrials.gov number, NCT02687113.