Monitoring vital signals is one of the most important tasks performed by the medical equipment nowadays. One of the most frequently applied monitoring types is the pulse oximetry, based on monitoring of arterial oxygen saturation. The design presented in this paper is a modular construction. One of the most important improvements is the application of the embedded PC module in the design of a pulse oximeter. The modular PC is using open source software. The system presented in this paper uses digital detector technology and the input pulse oximeter module developed by the Dolphin Medical. One of the main aims of the research team was to create a design capable of monitoring both vital signals of adults and newborns, to make the device suitable for pediatric and neonatologic wards. Performed tests and examinations for system stability as well as the comparision with other similar devices available on the market confirmed that the presented device design is an optimal solution for the described class of devices. By using the PC module the scalability of the system seriously increases comparing to other designs. The separation of the data acquisition part of the device and the analytical part of the device in the form of a personal computer module makes the device more alike medical computer system than the traditional pulse oximeter responsible only for visualiasation of the read data.
Each database system evolves during the time. If the primary database schema was designed only to store the limited scope of abstraction classes then the database system improvement process is performed in traditional way (using alter table, update table and create table commands). Anyhow it could be impossible, from the engineering point of view, or too expensive from the economic point of view. Transferring the data from one database schema to another database schema one has to perform an additional step called Data Cleaning. This paper present a basic sketch for the data cleaning theory based on the materialised views idea and corresponding data cleaning environment. The proposed methodology is suitable not only for the data verification but also for the reengineering of the broken references between data fields, recreation of missing rows and data types conversion.
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