IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0098450.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098450
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0098450&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0098450?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0098450. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.