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Automatic classifiers for the interpretation of electrocardiograms

C Abreu-Lima1, J P de Sá

  • 1Serviço de Medicina II, Faculdade de Medicina, Porto.

Revista Portuguesa De Cardiologia : Orgao Oficial Da Sociedade Portuguesa De Cardiologia = Portuguese Journal of Cardiology : an Official Journal of the Portuguese Society of Cardiology
|July 10, 1998
PubMed
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This study reviews automated electrocardiogram (ECG) interpretation, detailing feature extraction and classification methods. It compares logical and statistical classifiers for improved diagnostic accuracy in ECG analysis.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Computer Science

Background:

  • Electrocardiogram (ECG) diagnosis is a pattern recognition task, with clinician methods not fully understood.
  • Automated ECG interpretation models are employed, comprising feature extraction and classification phases.

Purpose of the Study:

  • To review feature selection techniques and classification strategies for automated ECG interpretation.
  • To evaluate performance metrics and compare results from different classification approaches.

Main Methods:

  • Review of feature extraction techniques for ECG signal measurements.
  • Analysis of classification strategies including heuristic (logical) and statistical classifiers.
  • Inclusion of artificial neural network techniques for signal classification.

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Main Results:

  • Comparison of logical and statistical ECG classifiers.
  • Discussion of performance evaluation methods and obtained results.
  • Overview of challenges and advancements in automated ECG interpretation.

Conclusions:

  • Automated ECG interpretation involves distinct feature extraction and classification steps.
  • Various classification approaches, including logical, statistical, and neural networks, are utilized.
  • The review provides insights into the relative merits of different ECG classification strategies.