Title :
Expert capnogram analysis
Author :
Bao, Weihan ; King, P. ; Zheng, P. King J ; Smith, B.E.
Author_Institution :
Vanderbilt Univ., Nashville, TN, USA
fDate :
3/1/1992 12:00:00 AM
Abstract :
A real-time expert system that deciphers CO/sub 2/ waveforms (capnograms) is described. The Capnogram Analyzer Expert System (CAES) was designed using both traditional pattern-recognition methods and an artificial intelligence (AI) approach for signal description and classification. The pattern-recognition technique is used to extract features from the digitized CO/sub 2/ waveforms. The AI approach involves abstracting CO/sub 2/ waveforms from numeric representation to higher-level symbolic representation and a so-called reasoning step to analyze the symbolic data. The CAES consists of three essential components: segmentation, single breath cycle identification and waveform classification. Each component is an expert in itself and is responsible for abstracting the waveform information from a lower level to a higher level using its own domain-specific knowledge base.<>
Keywords :
computerised pattern recognition; expert systems; real-time systems; waveform analysis; CAES; CO/sub 2/; Capnogram Analyzer Expert System; artificial intelligence; domain-specific knowledge base; expert capnogram analysis; numeric representation; pattern-recognition methods; real-time expert system; reasoning step; segmentation; signal description; single breath cycle identification; symbolic data; symbolic representation; waveform classification; waveforms; Anesthesia; Biomedical measurements; Biomedical monitoring; Computerized monitoring; Diagnostic expert systems; Expert systems; Instruments; Patient monitoring; Real time systems; Ventilation;
Journal_Title :
Engineering in Medicine and Biology Magazine, IEEE
