• DocumentCode
    1346331
  • Title

    Clustering ECG complexes using Hermite functions and self-organizing maps

  • Author

    Lagerholm, Martin ; Peterson, Carsten ; Braccini, Guido ; Edenbrandt, Lars ; Sörnmo, Leif

  • Author_Institution
    Dept. of Theor. Phys., Lund Univ., Sweden
  • Volume
    47
  • Issue
    7
  • fYear
    2000
  • fDate
    7/1/2000 12:00:00 AM
  • Firstpage
    838
  • Lastpage
    848
  • Abstract
    An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN´s). Each QRS complex is decomposed into Hermite basis functions and the resulting coefficients and width parameter are used to represent the complex. By means of this representation, unsupervised self-organizing NNs are employed to cluster the data into 25 groups. Using the MIT-BIH arrhythmia database, the resulting clusters are found to exhibit a very low degree of misclassification (1.5%). The integrated method outperforms, on the MIT-BIH database, both a published supervised learning method as well as a conventional template cross-correlation clustering method
  • Keywords
    electrocardiography; medical signal processing; self-organising feature maps; ECG complexes clustering; Hermite functions; MIT-BIH arrhythmia database; conventional template cross-correlation clustering method; electrodiagnostics; integrated method; misclassification; supervised learning method; width parameter; Artificial neural networks; Clustering methods; Databases; Electrocardiography; Neural networks; Physics; Self organizing feature maps; Self-organizing networks; Signal processing; Supervised learning;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/10.846677
  • Filename
    846677