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Knowledge-Based Event Detection in Complex Time Series Data

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Artificial Intelligence in Medicine (AIMDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1620))

Abstract

This paper describes an approach to the detection of events in complex, multi-channel, high frequency data. The example used is that of detecting the re-siting of a transcutaneous O2/CO2 probe on a baby in a neonatal intensive care unit (ICU) from the available monitor data. A software workbench has been developed which enables the expert clinician to display the data and to mark up features of interest. This knowledge is then used to define the parameters for a pattern matcher which runs over a set of intervals derived from the raw data by a new iterative interval merging algorithm. The approach has been tested on a set of 45 probe changes; the preliminary results are encouraging, with an accuracy of identification of 89%.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3UE

    Jim Hunter

  2. Department of Child Life and Health, University of Edinburgh, Edinburgh

    Neil McIntosh

Authors
  1. Jim Hunter
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  2. Neil McIntosh
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Editor information

Editors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Werner Horn

  2. Department of Medicine and Computer Science, Stanford University, 251 Campus Drive, Stanford, CA, 94305-5479, USA

    Yuval Shahar

  3. Department of Medicine Karolinska Institutet, Huddinge University Hospital, Sweden

    Greger Lindberg

  4. Department of Medical Informatics and Image Analysis, Aalborg University, Fredrik Bajers Vej 7D, DK-9000, Aalborg, Denmark

    Steen Andreassen

  5. School of Public Policy, University College London, 29/30 Tavistock Square, London, WC1H 9EZ, UK

    Jeremy Wyatt

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© 1999 Springer-Verlag Berlin Heidelberg

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Hunter, J., McIntosh, N. (1999). Knowledge-Based Event Detection in Complex Time Series Data. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_30

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  • DOI: https://doi.org/10.1007/3-540-48720-4_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66162-7

  • Online ISBN: 978-3-540-48720-3

  • eBook Packages: Springer Book Archive

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