Abstract

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.

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