Analyzing and learning ECG waveforms

Gabriella Kókai, Zoltán Alexin, T. Gyimóthy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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. 4.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages127-145
Number of pages19
Volume1314
ISBN (Print)3540634940, 9783540634942
Publication statusPublished - 1997
Event6th International Workshop on Inductive Logic Programming, ILP-1996 - Stockholm, Sweden
Duration: Aug 26 1996Aug 28 1996

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1314
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Workshop on Inductive Logic Programming, ILP-1996
CountrySweden
CityStockholm
Period8/26/968/28/96

Fingerprint

Electrocardiography
Waveform
Inductive logic programming (ILP)
Prolog
Debugging
Classifiers
Attribute Grammars
Specifications
Extended Systems
Unfolding
Classifier
Integrate
Specification
Electrocardiogram
Learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kókai, G., Alexin, Z., & Gyimóthy, T. (1997). Analyzing and learning ECG waveforms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1314, pp. 127-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1314). Springer Verlag.

Analyzing and learning ECG waveforms. / Kókai, Gabriella; Alexin, Zoltán; Gyimóthy, T.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1314 Springer Verlag, 1997. p. 127-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1314).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kókai, G, Alexin, Z & Gyimóthy, T 1997, Analyzing and learning ECG waveforms. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1314, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1314, Springer Verlag, pp. 127-145, 6th International Workshop on Inductive Logic Programming, ILP-1996, Stockholm, Sweden, 8/26/96.
Kókai G, Alexin Z, Gyimóthy T. Analyzing and learning ECG waveforms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1314. Springer Verlag. 1997. p. 127-145. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Kókai, Gabriella ; Alexin, Zoltán ; Gyimóthy, T. / Analyzing and learning ECG waveforms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1314 Springer Verlag, 1997. pp. 127-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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