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.

Classification of cardiac arrhythmias using competitive networks.

Information generated by sensors that collect a patient's 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.

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.