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The purpose of the paper is to analyze the ventricular tachycardia by soft computing. Ventricular tachycardia is a type of regular and fast heart rate which arises from improper electrical activity in the ventricles of the heart.

In this study, a soft computing approach was applied for the ventricular tachycardia detection. The soft computing was used to detect which factors are the most important for the ventricular tachycardia.

Three factors were used: brain natriuretic peptide, troponin I which is a part of the troponin complex and C-reactive protein which is an annular (ring-shaped), pentameric protein found in blood plasma.

It was found that troponin I has the most influence on the ventricular tachycardia prediction.

A novel method has been proposed to reduce the false alarm rate of arrhythmia patients regarding life-threatening conditions in the intensive care unit. In this purpose, the atrial blood pressure, photoplethysmogram (PLETH), electrocardiogram (ECG) and respiratory (RESP) signals are considered as input signals.

Three machine learning approaches feed-forward artificial neural network (ANN), ensemble learning method and k-nearest neighbors searching methods are used to detect the false alarm. The proposed method has been implemented using Arduino and MATLAB/SIMULINK for real-time ICU-arrhythmia patients' monitoring data.

The proposed method detects the false alarm with an accuracy of 99.4 per cent during asystole, 100 per cent during ventricular flutter, 98.5 per cent during ventricular tachycardia, 99.6 per cent during bradycardia and 100 per cent during tachycardia. The proposed framework is adaptive in many scenarios, easy to implement, computationally friendly and highly accurate and robust with overfitting issue.

As ECG signals consisting with PQRST wave, any deviation from the normal pattern may signify some alarming conditions. These deviations can be utilized as input to classifiers for the detection of false alarms; hence, there is no need for other feature extraction techniques. Feed-forward ANN with the Lavenberg–Marquardt algorithm has shown higher rate of convergence than other neural network algorithms which helps provide better accuracy with no overfitting.

This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of ‘concept strings’ from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

The purpose of this paper is to review the Braidwood Commission's two reports on the use of TASER conducted energy weapons in Canada and the death of Robert Dziekanski to determine whether the Commission's conclusions and subsequent recommendations constitute sound evidence‐based public policy.

This study analyzes Commissioner Braidwood's eight findings from the first report regarding the medical implications of the use of TASER devices by comparing those findings to the body of scientific, medical, and technical literature on the physiological effects of TASER technology. Additionally, this study reviews the potential ramifications of the Commissioner's recommendations regarding the use of TASER devices in both reports.

Evidence from the existing literature does not support the Commission's findings regarding the medical risks of the use of TASER technology. Recommendations to restrict the use of TASER devices are unlikely to reduce arrest‐related deaths, but they are likely to result in increased injuries to officers and suspects. Other recommendations, including training standards, testing requirements, reporting requirements, medical assistance, and research and review, are consistent with other reviews on the use of TASER technology and are necessary and appropriate to restore public confidence in police use‐of‐force.

The Braidwood Commission recommendations have had an immediate impact on the policies of several police agencies in Canada, including the Royal Canadian Mounted Police, but this study is the first critically to review whether those recommendations constitute formulation of sound evidence‐based public policy.

Ventricular surface activation time maps are estimated from simulated and measured body surface potential (BSP) maps and extra‐corporal magnetic field maps. In a first step the transfer matrix, relating the primary cardiac sources to the measured potential and/or magnetic field data, is calculated applying the boundary element method. Activation times are determined by minimizing a cost function which is based on this transfer matrix. This optimization method is solved by a quasi Newton method. The critical point theorem is used in order to estimate the starting column matrix.

Automatic internal cardiac defibrillators have various indications for placement. However, some patients may not fully benefit from this technology and the devices are expensive. Consequently, the aim of this paper is to describe a development model for clinical decision support to help providers offer their patients a more effective decision‐making process.

A decision tree was built based on previous trials described in the cardiac literature.

A decision‐making model for implanting these expensive but lifesaving devices is developed and a model for testing them (pre‐ and post‐implantation) is described.

The model could be used to develop prospective trials.

The paper demonstrates that the project's goal is better quality and cost‐effective care.

Discusses environmental factors which may have harmful effects on the cardiovascular system and cause acute or chronic disease. Classifies these factors as chemical, physical, biological and psychosocial. Concentrates on describing the chemical, physical and biological elements which directly cause cardiovascular diseases, such as nicotine and carbon monoxide (chemical); temperatue and electricity (physical) and viral infections such as maternal coxsackie (biological). Concludes by stressing the need for more intensive studies on this subject.

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

This study has demonstrated how technology may contribute to integrated care solutions by comparing conventional ward telemetry (WT) to a wearable ECG monitor (S-Patch) to detect atrial fibrillation (AF) in patients with stroke.

51 patients admitted for stroke workup were recruited across two major tertiary centres to compare WT monitoring for two days versus S-Patch for four days in the detection of AF. The efficacy to detect AF using both technologies was assessed via data extractions and medical officer review. A matrix was used to measure nursing/patient satisfaction and setup/resource times were assessed.

Patients (84–94%) and nursing staff (75–95%) preferred the S-Patch wearable technology. Non-parametric tests indicated significant time saving for removal of S-Patch versus WT [2.2 min vs 5.1 min (p = 0.00)]. Efficacy of S-Patch to detect AF following medical officer review was greater than WT, with seven patients identified with AF by S-Patch versus one using WT. The S-patch had a false positive rate of 78%.

The S-Patch is sensitive in the detection of AF; however, it showed a high false-positive rate with automated reporting. This study has provided insight into the details of delivery of integrated healthcare using wearable technology.

The technology and partnership were the first-in-kind in Australia. The S-Patch had a higher detection rate of AF compared to WT which allows patients to be anti-coagulated appropriately for the prevention of further stroke. The results of this study will be ideally placed to inform future policy in integrated healthcare using new technologies.