Analyzing and Learning ECG Waveforms

Gabriella Kkai1, Zoltn Alexin2, and Tibor Gyimthy3

1 Institute of Informatics, Jzsef Attila University

rpd tr 2, H-6720 Szeged, Hungary
Phone: (36) +(62) 311184, Fax: (36) +(62) 312292
e-mail: kokai@inf.u-szeged.hu

now visiting the Chair of Programming Languages,
Department of Computer Science,
Friedrich-Alexander University of Erlangen-Nrnberg
Martenstr. 3. D-91058 Erlangen, Germany
e-mail: kokai@informatik.uni-erlangen.de

2 Department of Applied Informatics, Jzsef Attila University
rpd tr 2, H-6720 Szeged, Hungary
Phone: (36) +(62) 311184, Fax: (36) +(62) 312292
e-mail: alexin@inf.u-szeged.hu

3 Research Group on Artificial Intelligence
Hungarian Academy of Sciences,
Aradi vrtanuk tere 1, H-6720 Szeged, Hungary
Phone: (36) +(62) 464139, Fax: (36) +(62) 312508
e-mail: gyimi@inf.u-szeged.hu.


Abstract. In this paper we present a system which integrates an ECG
waveform classifier (called PECG) with an interactive learner (called
IMPUT). The PECG system is based on an attribute grammar specification 
of ECGs that has been transformed to Prolog. The IMPUT
system combines the interactive debugging technique IDT with the unfolding 
algorithm introduced in SPECTRE. The main result achieved in
the new version of the PECG system is that an ILP method can be used
to improve the effectiveness of a real size Prolog application. Applying
the IMPUT method, the extended PECG system is able to suggest a correct 
solution to the user to replace the buggy clause recognized during
the debugging process. 
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