Computational depth of anesthesia via multiple vital signs based on artificial neural networks

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.This research is financially supported by the Ministry of Science and Technology (MOST) of Taiwan. This research is also supported by the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is also sponsored by MOST (MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the p roposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.variou

Non-Invasive Hemodynamics Monitoring System Based on Electrocardiography via Deep Convolutional Autoencoder

Data Availability Statement: This study utilizes the publicly available dataset, from https://physionet.org, accessed on 14 January 2021.Copyright: © 2021 by the authors. This study evaluates cardiovascular and cerebral hemodynamics systems by only using non-invasive electrocardiography (ECG) signals. The Massachusetts General Hospital/Marquette Foundation (MGH/MF) and Cerebral Hemodynamic Autoregulatory Information System Database (CHARIS DB) from the PhysioNet database are used for cardiovascular and cerebral hemodynamics, respectively. For cardiovascular hemodynamics, the ECG is used for generating the arterial blood pressure (ABP), central venous pressure (CVP), and pulmonary arterial pressure (PAP). Meanwhile, for cerebral hemodynamics, the ECG is utilized for the intracranial pressure (ICP) generator. A deep convolutional autoencoder system is applied for this study. The cross-validation method with Pearson's linear correlation (R), root mean squared error (RMSE), and mean absolute error (MAE) are measured for the evaluations. Initially, the ECG is used to generate the cardiovascular waveform. For the ABP system-the systolic blood pressure (SBP) and diastolic blood pressures (DBP)-the R evaluations are 0.894 ± 0.004 and 0.881 ± 0.005, respectively. The MAE evaluations for SBP and DBP are, respectively, 6.645 ± 0.353 mmHg and 3.210 ± 0.104 mmHg. Furthermore, for the PAP system-the systolic and diastolic pressures-the R evaluations are 0.864 ± 0.003 mmHg and 0.817 ± 0.006 mmHg, respectively. The MAE evaluations for systolic and diastolic pressures are, respectively, 3.847 ± 0.136 mmHg and 2.964 ± 0.181 mmHg. Meanwhile, the mean CVP evaluations are 0.916 ± 0.001, 2.220 ± 0.039 mmHg, and 1.329 ± 0.036 mmHg, respectively, for R, RMSE, and MAE. For the mean ICP evaluation in cerebral hemodynamics, the R and MAE evaluations are 0.914 ± 0.003 and 2.404 ± 0.043 mmHg, respectively. This study, as a proof of concept, concludes that the non-invasive cardiovascular and cerebral hemodynamics systems can be potentially investigated by only using the ECG signal.Funding: This research received no external funding

SiC Nanorods Grown on Electrospun Nanofibers Using Tb as Catalyst: Fabrication, Characterization, and Photoluminescence Properties

Well-crystallizedβ-SiC nanorods grown on electrospun nanofibers were synthesized by carbothermal reduction of Tb doped SiO2(SiO2:Tb) nanofibers at 1,250 °C. The as-synthesized SiC nanorods were 100–300 nm in diameter and 2–3 μm in length. Scanning electron microscopy (SEM) results suggested that the growth of the SiC nanorods should be governed by vapor-liquid-solid (VLS) mechanism with Tb metal as catalyst. Tb(NO3)3particles on the surface of the electrospun nanofibers were decomposed at 500 °C and later reduced to the formation of Tb nanoclusters at 1,200 °C, and finally the formation of a Si–C–Tb ally droplet will stimulate the VLS growth at 1,250 °C. Microstructure of the nanorod was further investigated by transmission electron microscopy (TEM). It was found that SiC <111> is the preferred initial growth direction. The liquid droplet was identified to be Si86Tb14, which acted as effective catalyst. Strong green emissions were observed from the SiC nanorod samples. Four characteristic photoluminescence (PL) peaks of Tb ions were also identified

Computational efficiency improvement for analyzing bending and tensile behavior of woven fabric using strain smoothing method

The tensile and bending behavior of woven fabrics are among the most important characteristics in complex deformation analysis and modelling of textile fabrics and they govern many aesthetics and performance aspects such as wrinkle/buckle, hand and drape. In this paper, a numerical method for analyzing of the tensile and bending behavior of plain-woven fabric structure was developed. The formulated model is based on the first-order shear deformation theory (FSDT) for a four-node quadrilateral element (Q4) and a strain smoothing method in finite elements, referred as a cell-based smoothed finite element method (CS-FEM). The physical and low-stress mechanical parameters of the fabric were obtained through the fabric objective measurement technology (FOM) using the Kawabata evaluation system for fabrics (KES-FB). The results show that the applied numerical method provides higher efficiency in computation in terms of central processing unit (CPU) time than the conventional finite element method (FEM) because the evaluation of compatible strain fields of Q4 element in CS-FEM model is constants, and it was also appropriated for numerical modelling and simulation of mechanical deformation behavior such as tensile and bending of woven fabric.The author (UMINHO/BPD/9/2017) and co-authors acknowledge the FCT funding from FCT – Foundation for Science and Technology within the scope of the project “PEST UID/CTM/00264; POCI-01-0145-FEDER-007136”

Aggregated impact of allowance allocation and power dispatching on emission reduction

Climate change has become one of the most important issues for the sustainable development of social well-being. China has made great efforts in reducing CO2 emissions and promoting clean energy. Pilot Emission Trading Systems (ETSs) have been launched in two provinces and five cities in China, and a national level ETS will be implemented in the third quarter of 2017, with preparations for China’s national ETS now well under way. In the meantime, a new round of China’s electric power system reform has entered the implementation stage. Policy variables from both electricity and emission markets will impose potential risks on the operation of generation companies (GenCos). Under this situation, by selecting key variables in each domain, this paper analyzes the combined effects of different allowance allocation methods and power dispatching models on power system emission. Key parameters are set based on a provincial power system in China, and the case studies are conducted based on dynamic simulation platform for macro-energy systems (DSMES) software developed by the authors. The selected power dispatching models include planned dispatch, energy saving power generation dispatch and economic dispatch. The selected initial allowance allocation methods in the emission market include the grandfathering method based on historical emissions and the benchmarking method based on actual output. Based on the simulation results and discussions, several policy implications are highlighted to help to design an effective emission market in China