Clustering ECG complexes using Hermite functions and self-organizing maps

Research output: Contribution to journalArticle

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.

Details

Authors
  • M Lagerholm
  • Carsten Peterson
  • G. Braccini
  • Lars Edenbrandt
  • Leif Sörnmo
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Medical Engineering
Original languageEnglish
Pages (from-to)838-848
JournalIEEE Transactions on Biomedical Engineering
Volume47
Issue number7
Publication statusPublished - 2000
Publication categoryResearch
Peer-reviewedYes