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.
Reactive oxygen species (ROS) are produced as a result of biochemical processes that are not in balance with the body's antioxidant defense mechanism. This metabolic dysfunction is referred to the oxidative stress (OS). Metabolic dysfunction-associated diseases are affected by changes in the redox balance. It is now widely recognized that oxidative stress significantly affects diabetes mellitus (DM), particularly type 2 diabetes. The biochemical changes associated with DM could disturb the oxidative milieu, leading to several microvascular complications in diabetic patients. Thus, DM is a perfect disease to explore the harmful consequences of oxidative stress and how to treat it. Oxidative stress triggered by hyperglycemia is
... Show MoreThis comprehensive review examines the efficacy and safety of tumor necrosis factor-alpha (TNF-α) inhibitors in treating various autoimmune diseases, and focuses on their application in Iraqi patients. Elevated TNF-α levels are linked to autoimmune disorders, leading to the development of anti-TNF-α therapies such as infliximab, etanercept, adalimumab, certolizumab pegol, and golimumab, which have gained FDA approval for conditions like psoriasis, in¬flammatory bowel disease, ankylosing spondylitis, and rheumatoid arthritis. While these therapies demonstrate sig¬nificant therapeutic benefits, including improved quality of life and disease management, they also carry risks, such as increased susceptibility to infections and pote
... Show MoreThe research aims to develop a proposed mechanism for financial reporting on sustainable investment that takes the specificity of these investments.
To achieve this goal, the researcher used (what if scenario) where the future financial statements were prepared for the year 2026, after completion of the sustainable project and operation, as the project requires four years to be completed.
The researcher relied on the results of the researchers collected from various modern sources relevant to the research topic and published on the internet, and the financial data and information obtained to assess the reality of the company's activity and its environmental, social, and economic i
... Show MoreThe "Nudge" Theory is considered one of the most recent theories, which is clear in the economic, health, and educational sectors, due to the intensity of studies on it and its applications, but it has not yet been included in crime prevention studies. The use of Nudge theory appears to enrich the theory in the field of crime prevention, and to provide modern, effective, and implementable mechanisms.
The study deals with the "integrative review" approach, which is a distinctive form of research that generates new knowledge on a topic through reviewing, criticizing, and synthesizing representative literature on the topic in an integrated manner so that new frameworks and perspectives are created around it.
The study is bas
... Show MoreThe research aims to determine the role of the practices of green human resources management in achieving requirements of environmental citizenship in the workplace, the General Company for Vegetable Oils was chosen for the application of field-side of research which represent one of the important industrial companies in Iraq, which suffers from poor Green human resources management applications, which reflected negatively on the development Environmental citizenship among Employees. The questionnaire use as a tool to collect data and information as well as field presence of the researcher, The research sample included (30) managers of departments and Division ,and through using statistical program (SPSS) the data has been analys
... Show MoreThis research aims to identify the role of radio advertising in promoting purchasing decisions of Iraqi audiences, especially that the placement of the announcement through the Iraqi radio stations dealt with a little of Iraqi researchers, and the goal of the research to know the extent of public exposure to the radio advertising through Iraqi radio and patterns of exposure,disclosure of the reasons for public hearing of the radio advertisement, identifying the most important factors influencing the decision to buy in the radio advertisement, this research is descriptive in terms of type as the researcher used the survey method,questionnaire and scale for data and information collection, the sample was selected according to the purp
... Show MoreBackground: Behçet’s disease (BD) is a disorder of systemic inflammatory condition. Its important features are represented by recurrent oral, genital ulcerations and eye lesions. Aims. The purpose of the current study was to evaluate and compare cytological changes using morphometric analysis of the exfoliated buccal mucosal cells in Behçet’s disease patients and healthy controls, and to evaluate the clinical characteristics of Behçet’s disease. Methods. Twenty five Behçet’s disease patients have been compared to 25 healthy volunteers as a control group. Papanicolaou stain was used for staining the smears taken from buccal epithelial cells to be analyzed cytomorphometrically. The image analysis software has been used to
... Show MorePeripheral artery disease (PAD) is associated with increased oxidative stress and impaired endothelial function. Ticagrelor treatment improves antioxidant properties in addition to its antiplatelet effects. This study investigated the impact of Ticagrelor treatment on serum superoxide dismutase (SOD) levels and other biochemical parameters in PAD patients. It also evaluated the potential diagnostic accuracy and clinical utility of specific biomarkers based on receiver operating characteristic (ROC) analysis. Seventy individuals were categorized into healthy control (n=40), baseline PAD patients not on Ticagrelor (B-PAD, n=30), and same PAD patients after treated with Ticagrelor (A-PAD, n=30). Parameters measured included SOD concent
... Show MoreThe haplotype association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease.Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls.It starts with inferring haplotypes from genotypes followed by a haplotype co-classification and marginal screening for disease-associated haplotypes.Unfortunately,phasing uncertainty may have a strong effects on the haplotype co-classification and therefore on the accuracy of predicting risk haplotypes.Here,to address the issue,we propose an alternative approach:In Stage 1,we select potential risk genotypes inste
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