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Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

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  • Jakub Kuzilek
  • Vaclav Kremen
  • Filip Soucek
  • Lenka Lhotska

Abstract

We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

Suggested Citation

  • Jakub Kuzilek & Vaclav Kremen & Filip Soucek & Lenka Lhotska, 2014. "Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    • Handle: RePEc:plo:pone00:0098450
    DOI: 10.1371/journal.pone.0098450
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    Cited by:

    1. Karel Doubravsky & Mirko Dohnal, 2015. "Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    2. Amirhessam Tahmassebi & Amir H. Gandomi & Mieke H. J. Schulte & Anna E. Goudriaan & Simon Y. Foo & Anke Meyer-Baese, 2018. "Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification," Complexity, Hindawi, vol. 2018, pages 1-24, May.

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