Earticle

The Intensive Care Unit (ICU) bedside monitors present the medical staff with large amounts of continuous data which can create a number of challenges. If the data is transmitted as part of a telemedicine system then the large volume of data can put pressure on bandwidth and affect the quality of service of the network. Another challenge is that the large volume of data has to be interpreted by medical staff to make a patient state assessment. In this paper we propose a time series analysis technique called data wavelets to derive trends in the data – this acts as a form of data compression for telemedicine and improves the quality of service of a network and also facilitates clinical decision support in the form of qualitative reasoning for patient state assessment. Our approach has been successfully applied to cardiovascular data from a neonatal ICU.

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the UDAR data compression. A newly developed data compression approach to approximate the UDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become an important research topic for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original UDAR data. The results show that this method can be used for significant reduction of data set.