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An Ontology-Based Intelligent Agent for Respiratory Waveform Classification

  • Chang-Shing Lee
  • Mei-Hui Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

Abstract

This paper presents an ontology-based intelligent agent for respire-tory waveform classification to help the medical staff with the judging the respiratory waveform from the ventilator. We present the manual construction tool (MCT), the respiratory waveform ontology (RWO), and the intelligent classification agent (ICA) to implement the classification of the respiratory waveform. The MCT allows the medical experts to construct and store the fuzzy numbers of respiratory waveforms to the RWO. When the ICA receives an input respiratory waveform (IRW), it will retrieve the fuzzy numbers from the RWO to carry out the classification task. Next, the ICA will send the classified results to the medical experts to make a confirmation and store the classified results to the classified waveform repository (CWR). The experimental results show that our approach can classify the respiratory waveform effectively and efficiently.

Keywords

Fuzzy Number Medical Expert Ontology Learning Fuzzy Ontology Respiratory Waveform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Shing Lee
    • 1
  • Mei-Hui Wang
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational University of TainanTainanTaiwan

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