The internal jugular vein cannulation is a commonly used route for access to the central venous system for therapeutic and diagnostic purposes. Although the great majority of this venous puncture are ...successfully performed using anatomic landmark technique, serious complication such as pneumothorax, arterial puncture or hemothorax can occur during this procedure, particularly under difficult clinical condition. A simple, ultrasound-guided technique has been developed using a portable scanner with transducer optimized for venous access. The authors experience in over 100 cases of ultrasound-guided central venous cannulations has been described. Ultrasound guidance reduces the number of passes needed to puncture the vein, increases the success rate of venous puncture, and minimizes complications. Ultrasound guidance is usually successful in allowing performance of internal jugular vein cannulation when landmark technique fail.
In reaction of alpha-phenyl, alpha-p-chlorophenyl and alpha-m-chlorophenylsuccinic acid with various aminopyridines, N-pyridyl-substituted succinimides (compounds 1-14) were obtained. These compounds ...were investigated for their CNS activity. Compounds 1, 2, 5, 6 and 7 displayed anticonvulsant properties in the maximum electroshock test. Compounds 5 and 6 were also active in the pentetrazole test.
Sixteen new heterocyclic 1,5-benzodiazepine derivatives (compounds AN8-AN24) were screened for their central action. Compounds AN8-AN10 and AN17 strongly antagonized the action of pentetrazol, ...compounds AN10, AN14-AN17 and AN22 had potent antiserotonin properties, and compounds AN10, AN19, AN20 and AN23 markedly potentiated the action of DOPA.
•New methodology based on single lead and analysis of longer (10-s) ECG signal fragments is proposed.•New training based on genetic algorithm coupled with 10-fold cross-validation is employed.•17 ...classes: normal sinus rhythm + pacemaker rhythm + 15 cardiac disorders are recognized.•New feature extraction and selection based on PSD, DFT and GA are employed.•Recognition sensitivity at a level of 90.20% (98 errors per 1000 classifications) is promising.
This article presents an innovative research methodology that enables the efficient classification of cardiac disorders (17 classes) based on ECG signal analysis and an evolutionary-neural system.
From a social point of view, it is extremely important to prevent heart diseases, which are the most common cause of death worldwide. According to statistical data, 50 million people are at risk for cardiac diseases worldwide. The subject of ECG signal analysis is very popular. However, due to the great difficulty of the task undertaken, and high computational complexity of existing methods, there remains substantial work to perform.
This research collected 1000 fragments of ECG signals from the MIH-BIH Arrhythmia database for one lead, MLII, from 45 patients. An original methodology that consisted of the analysis of longer (10-s) fragments of the ECG signal was used (an average of 13 times less classifications). To enhance the characteristic features of the ECG signal, the spectral power density was estimated (using Welch’s method and a discrete Fourier transform). Genetic optimization of parameters and genetic selection of features were tested. Pre-processing, normalization, feature extraction and selection, cross-validation and machine learning algorithms (SVM, kNN, PNN, and RBFNN) were used.
The best evolutionary-neural system, based on the SVM classifier, obtained a recognition sensitivity of 17 myocardium dysfunctions at a level of 90.20% (98 errors per 1000 classifications, accuracy = 98.85%, specificity = 99.39%, time for classification of one sample = 0.0023 s). Against the background of the current scientific literature, these results are some of the best results to date.
•Novel DGHNL credit scoring prediction model is proposed using German credit data.•Devloped 29-layer model compries of various learners (2 types of SVM, kNN, PNN, Fuzzy system).•New genetic layered ...training is used to optimize the DGHNL system.•Obtained classification accuracy of 94.60% (54 errors / 1000).
Credit scoring (CS) is an effective and crucial approach used for risk management in banks and other financial institutions. It provides appropriate guidance on granting loans and reduces risks in the financial area. Hence, companies and banks are trying to use novel automated solutions to deal with CS challenge to protect their own finances and customers. Nowadays, different machine learning (ML) and data mining (DM) algorithms have been used to improve various aspects of CS prediction. In this paper, we introduce a novel methodology, named Deep Genetic Hierarchical Network of Learners (DGHNL). The proposed methodology comprises different types of learners, including Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Probabilistic Neural Networks (PNN), and fuzzy systems. The Statlog German (1000 instances) credit approval dataset available in the UCI machine learning repository is used to test the effectiveness of our model in the CS domain. Our DGHNL model encompasses five kinds of learners, two kinds of data normalization procedures, two extraction of features methods, three kinds of kernel functions, and three kinds of parameter optimizations. Furthermore, the model applies deep learning, ensemble learning, supervised training, layered learning, genetic selection of features (attributes), genetic optimization of learners parameters, and novel genetic layered training (selection of learners) approaches used along with the cross-validation (CV) training-testing method (stratified 10-fold). The novelty of our approach relies on a proper flow and fusion of information (DGHNL structure and its optimization). We show that the proposed DGHNL model with a 29-layer structure is capable to achieve the prediction accuracy of 94.60% (54 errors per 1000 classifications) for the Statlog German credit approval data. It is the best prediction performance for this well-known credit scoring dataset, compared to the existing work in the field.
The heart disease is one of the most serious health problems in today’s world. Over 50 million persons have cardiovascular diseases around the world. Our proposed work based on 744 segments of ECG ...signal is obtained from the MIT-BIH Arrhythmia database (strongly imbalanced data) for one lead (modified lead II), from 29 people. In this work, we have used long-duration (10 s) ECG signal segments (13 times less classifications/analysis). The spectral power density was estimated based on Welch’s method and discrete Fourier transform to strengthen the characteristic ECG signal features. Our main contribution is the design of a novel three-layer (48 + 4 + 1) deep genetic ensemble of classifiers (DGEC). Developed method is a hybrid which combines the advantages of: (1) ensemble learning, (2) deep learning, and (3) evolutionary computation. Novel system was developed by the fusion of three normalization types, four Hamming window widths, four classifiers types, stratified tenfold cross-validation, genetic feature (frequency components) selection, layered learning, genetic optimization of classifiers parameters, and new genetic layered training (expert votes selection) to connect classifiers. The developed DGEC system achieved a recognition sensitivity of 94.62% (40 errors/744 classifications), accuracy = 99.37%, specificity = 99.66% with classification time of single sample = 0.8736 (s) in detecting 17 arrhythmia ECG classes. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate the cardiac health immediately with highest precision.
This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bioelectrical signal that helps monitor the heart’s ...electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an
1score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.
In the recent decades, credit scoring has become a very important analytical resource for researchers and financial institutions around the world. It helps to boost both profitability and risk ...control since bank credits plays a significant role in the banking industry.
In this study, a novel approach based on deep genetic cascade ensemble of different support vector machine (SVM) classifiers (called Deep Genetic Cascade Ensembles of Classifiers (DGCEC)) is applied to the Statlog Australian data. The proposed approach is a hybrid model which merges the benefits of: (a) evolutionary computation, (b) ensemble learning, and (c) deep learning. The proposed approach comprises of a novel 16-layer genetic cascade ensemble of classifiers, having: two types of SVM classifiers, normalization techniques, feature extraction methods, three types of kernel functions, parameter optimizations, and stratified 10-fold cross-validation method. The general architecture of the proposed approach consists of ensemble learning, deep learning, layered learning, supervised training, feature (attributes) selection using genetic algorithm, optimization of parameters for all classifiers by using genetic algorithm, and a new genetic layered training technique (for selection of classifiers).
Our developed model achieved the highest prediction accuracy of 97.39%. Hence, our proposed approach can be employed in the banking system to evaluate the bank credits of the applicants and aid the bank managers in making correct decisions.
•A novel deep genetic cascade ensemble of SVM classifiers, DGCEC technique is proposed.•The DGCEC is used to predict statlog Australian credit approval to credit scoring.•A 16-layer cascade-based structure of DGCEC comprises of SVM classifiers.•A genetic layered training based on mimicking mechanism of tutoring is applied.•Obtained classification accuracy of 97.39% (18 errors/690).
Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system ...should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.
The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose ...COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse.
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