Bruno Gomes de Araújo

Classification of cardiac arrhythmias using competitive networks

Information generated by sensors that collect a patients vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Middleware for remote healthcare monitoring

The development of middleware has emerged as an area of expanding research, focused on the integration of services available for distributed applications. In this context, many challenges have also arisen with the use of middleware, such as communication, flexibility, performance, as well as integration with the Web and the computer itself. The development of middleware for mobile computing poses new challenges to developers because of the limitations of mobile devices. Thus, these developers must understand the new mobile computing and middleware technologies in order to integrate them. The objective of this work is to develop a service-oriented context-aware middleware for real-time data management in real-time using mobile devices.

Modeling of medical care with stochastic Petri Nets

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

Service-oriented biomedical devices

The great diversity in the architecture of biomedical devices, coupled with their different communication protocols, has hindered the implementation of systems that need access to these devices. Given these differences, arises the need to provide access to such devices in a transparent way. In this sense, this paper proposes an service-oriented architecture to access biomedical devices as a way to abstract the mechanism for writing and reading data on these devices, thus contributing to the increase in quality and productivity of biomedical systems in order to enable that the focus of the development team of biomedical software is almost exclusively intended for its functional requirements, i.e. business rules relevant to the problem domain. For the development of this work, technologies such as the XML language and the Java programming language were used and concepts of Software Engineering as Design Patterns. After the proposition of the architecture, tests in order to validate the proposed architecture were performed.

ILITIA: telehealth architecture for high-risk gestation classification

Introduction: According to the World Health Organization, about 9.2% of the 28 million newborns worldwide are stillborn. Besides, about 358,000 women died due to complications related to pregnancy in 2015. Part of these deaths could have been avoided with improving prenatal care agility to recognize problems during pregnancy. Based on that, many efforts have been made to provide technologies that can contribute to offer better access to information and assist in decision-making. In this context, this work presents an architecture to automate the classification and referral process of pregnant women between the basic health units and the referral hospital through a Telehealth platform. Methods: The Telehealth architecture was developed in three components: The data acquisition component, responsible for collecting and inserting data; the data processing component, which is the core of the architecture implemented using expert systems to classify gestational risk; and the post-processing component, in charge of the delivery and analysis of cases. Results: Acceptance test, system accuracy test based on rules and performance test were realized. For the tests, 1,380 referral forms of real situations were used. Conclusion: On the results obtained with the analysis of real data, ILITIA, the developed architecture has met the requirements to assist medical specialists on gestational risk classification, which decreases the inconvenience of pregnant women displacement and the resulting costs.

Mult-I/O - a middleware multi input and output for access devices: A case study applied the biomedical devices

The great diversity in the architecture of hardware devices allied to many communication protocols, has been hindering the implementation of systems that need to access these devices. Given these differences, it appears the need of providing the access of these devices in a transparent way. In this sense, the present work proposes a middleware, mult input and output for access the devices, as a way of abstracting the writing and reading data mechanisms in hardware devices, contributing this way, for increasing systems productivity, as the developers are just focused in their functional requirements.