An ontology-based intelligent agent for respiratory waveform classification

Authors:
Chang-Shing Lee;Mei-Hui Wang
Affiliations:
Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan;Department of Computer Science and Information Engineering, National University of Tainan, Tainan, Taiwan
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

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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.