@conference{228,
  author = {Silvia Miksch and Andreas Seyfang},
  title = {{Finding Intuitive Abstractions of High-Frequency Data}},
  abstract = {In this paper we describe ways to transform a curve constituted by a series of data points delivered by monitoring devices into a series of bends and lines between them. The resulting qualitative representation of the curve can easily be utilized in a Knowledge Based System. Since the representation obtained resembles the way humans describe curves, e.g. ``this bend is not sharp enough to be normal'', the transformation of the data described in this paper promises to facilitate knowledge acquisition in the eld of interpreting high-frequency data in the medical domain. Our analysis performs best on curves that are periodical but too irregular to be analyzed by Fast Fourier Transformation or other standard methods.},
  year = {1999},
  journal = {The First Workshop on Computers in Anaesthesia and Intensive Care: Knowledge-Based Information Management},
  month = {June 20.},
  publisher = {in conjunction with the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making (AIMDM'99)},
  url = {http://www.cvast.tuwien.ac.at/sites/default/files/publications/PDF/1999/caic_1999/mik_caic99.pdf},
  language = {eng},
}