A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree

  • Authors:

  • Themis P. Exarchos;Markos G. Tsipouras;Costas P. Exarchos;Costas Papaloukas;Dimitrios I. Fotiadis;Lampros K. Michalis

  • Affiliations:

  • Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Medical Physics, Medical School, ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Department of Biological Applications and Tech ...;Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, GR 45110 Ioannina, Greece and Biomedical Research Institute-FORTH, GR 45110 ...;Michaelideion Cardiology Centre, GR 45110 Ioannina, Greece and Department of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Objective: In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. Methods: The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from a decision tree induced from the training dataset, (b) transformation of the crisp set of rules into a fuzzy model and (c) optimization of the fuzzy model's parameters using global optimization. Material: The above methodology is employed in order to create fuzzy expert systems for ischaemic and arrhythmic beat classification in ECG recordings. The fuzzy expert system for ischaemic beat detection is evaluated in a cardiac beat dataset that was constructed using recordings from the European Society of Cardiology ST-T database. The arrhythmic beat classification fuzzy expert system is evaluated using the MIT-BIH arrhythmia database. Results: The fuzzy expert system for ischaemic beat classification reported 91% sensitivity and 92% specificity. The arrhythmic beat classification fuzzy expert system reported 96% average sensitivity and 99% average specificity for all categories. Conclusion: The proposed methodology provides high accuracy and the ability to interpret the decisions made. The fuzzy expert systems for ischaemic and arrhythmic beat classification compare well with previously reported results, indicating that they could be part of an overall clinical system for ECG analysis and diagnosis.