Rule-Based Learning for More Accurate ECG Analysis
- 1 July 1982
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4 (4) , 369-380
- https://doi.org/10.1109/tpami.1982.4767268
Abstract
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.Keywords
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