Issue No. 04 - April (1982 vol. 4)
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.
K. P. Birman, "Rule-Based Learning for More Accurate ECG Analysis," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 4, no. , pp. 369-380, 1982.