TIME SERIES PREDICTION FOR BIOMEDICAL MEASUREMENTS USING FUZZY LOGIC |
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| 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.
- Download:
- IMEKO-TC4-2004-079.pdf
- DOI:
- -
- 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