Accurate Classification of ECG Patterns with Subject-Dependent Feature Vector

  • Piotr AugustyniakEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)


Correct and accurate classification of ECG patterns in a long-term record requires optimal selection of feature vector. We propose a machine learning algorithm that learns from short randomly selected signal strips and, having an approval from a human operator, classifies all remaining patterns. We applied a genetic algorithm with aggressive mutation to select few most distinctive features of ECG signal. When applied to the MIT-BIH Arrhythmia Database records, the algorithm reduced the initial feature space of 57 elements to 3–5 features optimized for a particular subject. We also observe a significant reduction of misclassified beats percentage (from 2.7 % to 0.7 % in average for SVM classifier and three features) with regard to automatic correlation-based selection.


Feature Vector Mother Population Syntactic Model Signal Strip Heartbeat Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This scientific work is supported by the AGH University of Science and Technology in year 2015 as a research project No.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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