Using SEEK for multichannel pattern recognition.

The author's work on computerized analysis of the 2-channel, 24-hr electrocardiogram has previously resulted in the development of multichannel signal processing systems that learn by observation. A new tool for implementing such algorithms is described: the pattern recognition language SEEK. Programs written in SEEK build a knowledge base containing treelike data structures, each of which stores acquired information about a particular multichannel waveform. Input data are interpreted by performing an efficient parallel evaluation of the structures in the knowledge base. The work is applicable to a wide variety of pattern recognition problems that arise in medical signal processing. The approach is illustrated with examples drawn from ECG analysis.

Rule-Based Learning for More Accurate ECG Analysis.

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.