A Fuzzy Recognition Agent based on Ontology Model for Respiratory Waveform Processing

Abstract

In recent years, respiratory illnesses are increasingly recognized as a growing concern for healthcare workers (HCWs) and patients after severe acute respiratory syndrome (SARS) threatened to engulf the world in 2003. Besides, many health organizations also emphasize the importance of prevention and health promotion in helping health care systems to meet the challenges of an aging population around the world. Therefore, the respiratory healthcare plays an important role in the clinical care. This paper presents a fuzzy recognition agent based on ontology model for respiratory waveform processing, to classify a respiratory waveform into one of the four standard respiratory waveforms and provide appropriate information for the medical staff as a basis of diagnosis. This fuzzy recognition agent contains two modules, including the fuzzy classification agent and fuzzy matching agent. The experimental results show that our approach can classify and recognize the respiratory waveform effectively and efficiently.