The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.
Schiff bases (SBs) based on amino acid derivative stand for multipurpose ligands that formed by condensing amino acids with carbonyl groups. They are significant in pharmaceutical and medical areas due to their widespread biological actions such as antiseptic, antifungal, along with antitumor actions. Transition metallic complexes resulting from SB ligands with biological activity were extensively experimented in the literature. In this article, we review, in details, about synthesizing and biological performances of SBs along with its complexes.
In this review of literature, the light will be concentrated on the local drugs delivery systems for treating the periodontal diseases. Principles, types, advantages and indications of each type will be discussed in this paper.
Schiff bases, named after Hugo Schiff, are aldehyde- or ketone-like compounds in which the carbonyl group is replaced by imine or azomethine group. They are widely used for industrial purposes and also have a broad range of applications as antioxidants. An overview of antioxidant applications of Schiff bases and their complexes is discussed in this review. A brief history of the synthesis and reactivity of Schiff bases and their complexes is presented. Factors of antioxidants are illustrated and discussed. Copyright © 2016 John Wiley & Sons, Ltd.
The purpose of this study is to underline the progression and development of research regarding oxygen-containing heterocycles as well as the contribution that some oxygen-containing heterocycles have made as anticancer medicines. A series of publications about the antitumor effects of derivatives of heterocyclic compounds containing an oxygen atom, such as furan, benzofuran, oxazole, benzoxazole, and oxadiazole, were evaluated, and their anticancer activities showed encouraging results when compared to those of established standard treatments.
The main aim of image compression is to reduce the its size to be able for transforming and storage, therefore many methods appeared to compress the image, one of these methods is "Multilayer Perceptron ". Multilayer Perceptron (MLP) method which is artificial neural network based on the Back-Propagation algorithm for compressing the image. In case this algorithm depends upon the number of neurons in the hidden layer only the above mentioned will not be quite enough to reach the desired results, then we have to take into consideration the standards which the compression process depend on to get the best results. We have trained a group of TIFF images with the size of (256*256) in our research, compressed them by using MLP for each
... Show MoreAn eco-epidemic model is proposed in this paper. It is assumed that there is a stage structure in prey and disease in predator. Existence, uniqueness and bounded-ness of the solution for the system are studied. The existence of each possible steady state points is discussed. The local condition for stability near each steady state point is investigated. Finally, global dynamics of the proposed model is studied numerically.
The role of the climate in the development of the performance of the administrative bodies of sports clubs
This research set to indicate the role of the opportunity cost in the overall economic development (human and social development) by selecting the most appropriate alternative for the growth of the country in exchange for sacrificing profit limits to achieve this growth and development of the country, especially in the present circumstances of the country and after studying the reality of the economic case for him, as the problem lies with don't selecting the best alternative that enhances the gross domestic product, which extends to promote overall economic development and revive the industrial and agricultural sectors, productivity is more like Impotent, versus sacrifice alternative consumption may bring more financially lucrative than
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