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TIME SERIES PREDICTION FOR BIOMEDICAL MEASUREMENTS USING FUZZY LOGIC

Claudio De Capua, Emilia Romeo
  • Abstract:
    In this paper is proposed an algorithm of prediction fuzzy for chaotic time series. This approach has been select because, in presence of specific pathologies, biomedical data may be represented as a chaotic time series. In particular, we are interested in monitoring the intracranial pressure (IP) of some patients in a state of coma who were suffering from intracranial hypertension syndrome. In these particular cases, prediction is necessary (from a diagnostic point of view) if you want to operate at the right moment on IP abnormal conditions. The proposed approach is based on a prediction multi-factor algorithm which doesn’t need the knowledge of the mathematical working model of the biologic phenomenon, translating the real time series into a fuzzy time series.
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  • DOI:
    _unreg_tc4-2004.079

Event details:

  • IMEKO TC:
    TC4
  • Event name:
    TC4 Symposium 2004
  • Title:
    XIII IMEKO TC4 International Symposium on Measurements for Research and Industrial Applications (together with IXth International Workshop on ADC Modeling and Testing, IWADC)
  • Place:
    Athens, GREECE
  • Time:
    29 September 2004 - 01 October 2004