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According to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.

In this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.

Among these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.

In this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

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.

The research fields of service innovation and social innovation have, until now, been largely disconnected. At the most basic level, a great many social innovations are services, often public sector services with social entrepreneurs organizing and delivering service innovations. As well as this overlap in the focus of research, scholars in both research fields address socio-economic concerns using multidisciplinary perspectives. The purpose of this paper is to provide a framework that can bridge the two research fields.

Inter-linkages between service and social innovation are shown by identifying research areas in which both find a joint heuristic field. This approach has been illustrated in a set of case studies in the health sector in Europe.

The bridge between social innovation and service innovation research can be built when social innovation is examined through a multi-agent framework. The authors focus on social innovations where the co-creation of novel services is guided by the prominent position taken by citizens, social entrepreneurs or third sector organizations (NGOs or charities) in the innovation process. Of particular interest are the ways in which the interests of individual users and citizens are “represented” by third sector organizations.

The case study of the Austrian nationwide public access defibrillation programme provides an exemplar of the process of co-creation by which this social innovation was developed, implemented and sustained. Here the Austrian Red Cross acted on behalf of citizens, organizing an innovation network capable of creating both the demand and the supply side of a sustainable market for the production and safe application of portable automated external defibrillators (AEDs) in Austria. This process involved, first, raising public awareness of the need for portable defibrillators and acting as a user representative when inducing changes in the design of portable AEDs. Later, there was the institutionalization of AED training in every first aid training in Austria, work with local manufacturers to produce this device, and with large user organizations to install AEDs on their premises.

The paper develops multi-agent model of innovation that enables one to synthesize key concepts in social and service innovation literatures and, thereby, examine the dynamics of invention and diffusion of social innovations.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

Ubiquitous health-care monitoring systems can provide continuous surveillance to a person using various sensors, including wearables and implantable and fabric-woven sensors. By assessing the state of many physiological characteristics of the patient’s body, continuous monitoring can assist in preparing for the impending emergency. To address this issue, this study aims to propose a health-care system that integrates the treatment of the impending heart, stress and alcohol emergencies. For this purpose, this study uses readings from sensors used for electrocardiography, heart rate, respiration rate, blood alcohol content percentage and blood pressure of a patient’s body.

For heart status, stress level and alcohol detection, the parametric values obtained from these sensors are preprocessed and further divided into four, five and six phases, respectively. A final integrated emergency stage is derived from the stages that were interpreted to examine at a person’s state of emergency. A thorough analysis of the proposed model is carried out using four classification techniques, including decision trees, support vector machines, k nearest neighbors and ensemble classifiers. For all of the aforementioned detections, four metrics are used to evaluate performance: classification accuracy, precision, recall and fmeasure.

Eventually, results are validated against the existing health-care systems. The empirical results received reveal that the proposed model outperforms the existing health-care models in the context of metrics above for different detections taken into consideration.

This study proposes a health-care system capable of performing data processing using wearable sensors. It is of great importance for real-time systems. This study assures the originality of the proposed system.

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 seeks to identify 172 American elementary, middle, and high school teachers' perceptions of the major sources and intensity of the experience of mistreatment by a principal, the effects of such mistreatment, how these perceptions varied by demographic variables, teachers' coping skills, and teachers' perceptions of contributing factors.

Participants completed a piloted, validated online questionnaire.

The participants reported experiencing a wide range of abusive principal behaviors that resulted in serious or extensive harmful psychological/emotional, physical/physiological, and work‐related effects to themselves, their work, and their families. An overwhelming majority (77 percent) indicated they would leave their job for another because of the harm caused by the principal's mistreatment. Mistreated teachers typically did not enact problem‐focused coping strategies. Differences were found among teachers of various demographic categories for several variables.

The findings of this current, quantitative study expand the authors' earlier qualitative research on the topic of teacher mistreatment; these are the only studies on this topic completed in the USA. Practical implications and suggestions for future research are included.

The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications.

The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations.

The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation.

The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals.

The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style.

The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

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.

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.