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Rule-Based Learning for More Accurate ECG Analysis
April 1982 (vol. 4 no. 4)
pp. 369-380
Kenneth P. Birman, Computer Science Division, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720; 100 Wellington Avenue, New Rochelle, NY 10804.
Long-term electrocardiograms exhibit a small number of QRS morphologies (waveform shapes) whose analysis can reveal cardiac abnormalities. We considered the problem of accurately identifying instances of each in 24-h ECG recordings. A new learning algorithm was developed. Each QRS morphology is represented as a tree of rule activations, which associate attribute measurements with a rule. Each rule has a syntactic pattern together with a semantic procedure which manages and applies the knowledge stored in the activation. A single rule may be activated several times to learn different waveform segments. Delineation refinement improves each hypothesized signal interpretation. A simple conflict resolution mechanism resolves conflicting interpretations into a single unambiguous one. Comparison of the system with an existing program confirmed the promise of the new approach.
Citation:
Kenneth P. Birman, "Rule-Based Learning for More Accurate ECG Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, no. 4, pp. 369-380, April 1982, doi:10.1109/TPAMI.1982.4767268
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