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)

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

Data Availability Statement: This study utilizes the publicly available dataset, from https:// physionet.org, accessed on 22 June 2020.Copyright: © 2022 by the authors. In this paper, an effective electrocardiogram (ECG) recurrence plot (RP)-based arrhythmia classification algorithm that can be implemented in portable devices is presented. Public databases from PhysioNet were used to conduct this study including the MIT-BIH Atrial Fibrillation Database, the MIT-BIH Arrhythmia Database, the MIT-BIH Malignant Ventricular Ectopy Database, and the Creighton University Ventricular Tachyarrhythmia Database. ECG time series were segmented and converted using an RP, and two-dimensional images were used as inputs to the CNN classifiers. In this study, two-stage classification is proposed to improve the accuracy. The ResNet-18 architecture was applied to detect ventricular fibrillation (VF) and noise during the first stage, whereas normal, atrial fibrillation, premature atrial contraction, and premature ventricular contractions were de-tected using ResNet-50 in the second stage. The method was evaluated using 5-fold cross-validation which improved the results when compared to previous studies, achieving first and second stage average accuracies of 97.21% and 98.36%, sensitivities of 96.49% and 97.92%, positive predictive values of 95.54% and 98.20%, and F1-scores of 95.96% and 98.05%, respectively. Furthermore, a 5-fold improvement in the memory requirement was achieved when compared with a previous study, making this classifier feasible for use in resource-constricted environments such as portable devices. Even though the method is successful, first stage training requires combining four different arrhyth-mia types into one label (other), which generates more data for the other category than for VF and noise, thus creating a data imbalance that affects the first stage performance.Ministry of Science and Technology, Taiwan (grant number: MOST 110-2221-E-155-004-MY2)

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

Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography

Hypertension affects huge number of people around the world. It also has a great contribution to cardiovascular and renal related diseases. This study investigates the ability deep convolutional autoencoder (DCAE) to generate the continuous arterial blood pressure (ABP) by only utilizing the photoplethysmography (PPG) to generate the continuous ABP. The total of 18 patients is utilized. LeNet-5 and U-Net based DCAEs, respectively for LDCAE and UDCAE, are compared to the MP60 IntelliVue Patient Monitor, as the golden standard. Moreover, in order to investigate the data generalization, leave-one-out cross-validation (CV) method is conducted. The results show that the UDCAE provides superior results in producing the SBP estimation. Meanwhile, LDCAE gives a slightly better for the DBP prediction. Finally, the genetic algorithm (GA) based optimization deep convolutional autoencoder (GDCAE) is further administered to optimize the ensemble of the CV models. The results reveal that the GDCAE is superior to either the LDCAE or UDCAE. For conclusion, this study reveals that the SBP and DBP can also be accurately achieved by only utilizing the single PPG signal

Higher resolution input image of convolutional neural network of reinforced concrete earthquake-generated crack classification and localization

According United States Geological Survey, Aceh is the northwestern part in Indonesia that has been affected by numerous strong earthquakes since 2004 tsunami. These earthquakes have generated massive impact to the buildings around the area, especially for the reinforced concrete based buildings. One of the most important problems to the reinforced concrete is the earthquake-generated crack. In this study, the dataset from the normal and cracked reinforce concrete are collected by taking the normal and cracked images. Several convolutional neural network models are implemented such as LeNet based models. These models are initially applied to recognize either normal or cracked conditions. Eventually, for the last stage, the localization of the crack is visualized by imposing the original images. For the localization, this study also evaluates the relatively smaller and bigger cracks. The results show the higher input image with modified LeNet generates better results compared to the basic model in superimposing the localized crack.The authors would like to express their gratitude to Universitas Syiah Kuala for financial support partly for research through the Professor's research grant No. 03 / UN11.2 / PP / PNBP / SP3 / 2019

Computational fluid dynamics based fuzzy control optimization of heat exchanger via genetic Algorithm

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Big data analysis of emergency medical service applied to determine the survival rate effective factors and predict the ambulance time variables

Emergency medical service (EMS) takes an important part in out-of-hospital cases, and it takes decisively effect to patients’ mortality rate. There are five factors have been scrutinized in this paper with large database to determine the correlation and effectiveness to survival rate, and also the difference between urban and suburban area. Seven years from 2007 to 2013 emergency record have conducted in study. Via applying analysis of variance (ANOVA), age, gender, response time, on-scene time and transportation time were used to be the analysis condition in survival rate and urban/suburban difference. Furthermore, age, gender, population density and total ambulance number were used as inputs to predict time outputs of response time, on-scene time, transportation time with artificial neural network (ANN). There are significant differences in all five factors of 7 years analysis, with age have the highest correlation (Pearson = -0.059), and on-scene time second highest (Pearson = -0.033) to survival rate. For urban and suburban comparison, each city has the highest correlation with time factors, and transportation time has the highest among other time factors. For time prediction, the best model performs mean absolute error (MAE) of 3.2675 minutes, and response time has the lowest error of 2.2498 minutes. Observing the result, it is suggested that patient with male around or higher 65 years old should be more concern and urgency. Urban and suburban do affect the out-of-hospital internal time in the study because urban patient spend less time on transportation time but more on on-scene time, while suburban has the opposite trend. In experimental prediction, model built with 4 years database could made the prediction within 3.2 minutes in training city but unable to apply to different city as well

Cardiopulmonary resuscitation Ppattern evaluation based on ensemble empirical mode decomposition filter via non-linear approaches

Out-of-hospital cardiac arrest (OHCA) is a critical cardiac disorder. The OHCA survival rate is still relatively low. Cardiopulmonary resuscitation (CPR) is very essential with the cardiac arrest. This study evaluates a non-linear approximation of the CPR given to patients, especially asystole patients. In order to clean the electrocardiography (ECG) signal which is collected by the automated external defibrillator (AED), the raw signal is filtered using ensemble empirical mode decomposition (EEMD), and the CPR-related IMFs are chosen to be evaluated. Sample entropy (SE), complexity index (CI), detrended fluctuation algorithm (DFA) and statistical analysis using Anova are utilized. The CPR evaluation compares the patient survival rates after two hours of the cardiac arrest. The CPR pattern of the 951 asystole patients are analyzed. In the CPR-related IMFs peak-to-peak interval analysis, for both classes, patient groups who are younger than or older than 60 years, does not have any significance. Furthermore, the amplitude difference evaluation, both classes do not have any significant difference for SE (p = 0.28) and DFA (p = 0.92) except for the CI (p = 0.028). The results show that patients group aged younger than 60 years have higher survival rate with high complexity of the CPR-IMFs amplitude differences.This research is financially supported by the Ministry of science and technology (MOST) of Taiwan (MOST103-2627-M-155-001)

Automatic IHR-based sleep stage detection using features of residual neural network

Data availability; The authors do not have permission to share data.Untreated sleep disorders can harm bodily functions, and a sleep study and monitoring of sleep stages are the first steps in diagnosing these disorders. Using Polysomnography (PSG), signal scoring for sleep stage determination has become a familiar investigation in recent years. Despite its effectiveness, the procedure is time-consuming and costly. This study presents a cost-effective method for sleep classification based on Electrocardiogram (ECG) input signals. We proposed a multi-ethnic study of the Atherosclerosis dataset, including 1700 PSG, to develop a Residual Neural Network (RNN) classifier to stage sleep from Instantaneous Heart Rate (IHR) extracted from the ECG signals. The proposed system follows the following steps: ECG collection, signal preprocessing (including ECG normalization and segmentation, instant heart rate calculation and normalization, resampling, and filtering), and classification using an RNN. A Convolutional Neural Network (CNN) is used to detect sleep stages using preprocessed segments of the IHR time series of 240 samples centered on 30-s epochs as inputs. The proposed algorithm in the five-fold cross-validation achieved an accuracy of 85.32%, a kappa of 77.11%, a Sensitivity of 81.14%, a Specificity of 82.68%, and an F-1 score of 81.87%. The results show that ECG data provide valuable information about sleep stages for a large population.Ministry of Science and Technology, Taiwan (Grant number: MOST 107-2221-E-155-009-MY2)