The Application of Genetic Algorithm for Unsupervised Classification of ECG

  • Roshan Joy MartisEmail author
  • Hari Prasad
  • Chandan Chakraborty
  • Ajoy Kumar Ray
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)


In this chapter,we have proposed an integrated methodology for electrocardiogram (ECG) based differentiation of arrhythmia and normal sinus rhythm using genetic algorithm optimized k-means clustering. Open source databases consisting of the MIT BIH arrhythmia and MIT BIH normal sinus rhythm data are used. The methodology consists of QRS-complex detection using the Pan-Tompkins algorithm, principal component analysis (PCA), and subsequent pattern classification using the k-means classifier, error back propagation neural network (EBPNN) classifier, and genetic algorithm optimized k-means clustering. The m-fold cross-validation scheme is used in choosing the training and testing sets for classification. The k-means classifier provides an average accuracy of 91.21 % over all folds, whereas EBPNN provides a greater average accuracy of 95.79 %. In the proposed method, the k-means classifier is optimized using the genetic algorithm (GA), and the accuracy of this classifier is 95.79 %, which is equal to that of EBPNN. In conclusion, the classification accuracy of simple unsupervised classifiers can be increased to near that of supervised classifiers by optimization using GA. The application of GA to other unsupervised algorithms to yield higher accuracy as a future direction is also observed.


Electrocardiogram Principal component analysis Neural network Genetic algorithm MIT-BIH database 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Roshan Joy Martis
    • 1
    Email author
  • Hari Prasad
    • 1
  • Chandan Chakraborty
    • 1
  • Ajoy Kumar Ray
    • 2
  1. 1.School of Medical Science and TechnologyIITKharagpurIndia
  2. 2.Department of Electronics and Electrical Communication EngineeringIITKharagpurIndia

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