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dc.contributor.authorBirman, Kenneth P.en_US
dc.date.accessioned2007-04-23T16:46:11Z
dc.date.available2007-04-23T16:46:11Z
dc.date.issued1982-10en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR82-529en_US
dc.identifier.urihttps://hdl.handle.net/1813/6368
dc.description.abstractOur work on computerized analysis of the 2-channel, 24-hr electrocardiogram has resulted in the development of multi-channel signal processing systems that learn by observation. In this paper a new tool for implementing such algorithms is described: the pattern recognition language $\cal SEEK$. Programs written in $\cal SEEK$ build a knowledge base containing tree-like data structures, each of which stores acquired information about a particular multi-channel waveform. Input data is interpreted by performing an efficient parallel evaluation of the structures in the knowledge base. Our 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.en_US
dc.format.extent3054084 bytes
dc.format.extent707657 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleUsing $\cal SEEK$ for Multi-Channel Pattern Recognitionen_US
dc.typetechnical reporten_US


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