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Clustering ECG complexes using Hermite functions and self-organizing maps

Lagerholm, M; Peterson, Carsten LU ; Braccini, G.; Edenbrandt, Lars LU and Sörnmo, Leif LU (2000) In IEEE Transactions on Biomedical Engineering 47(7). p.838-848
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.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Biomedical Engineering
volume
47
issue
7
pages
838 - 848
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • Scopus:0033918208
ISSN
0018-9294
DOI
10.1109/10.846677
language
English
LU publication?
yes
id
d005115c-7ca6-4360-9683-e729f5f5957f (old id 1746323)
date added to LUP
2010-12-16 15:47:17
date last changed
2016-11-27 04:30:01
@misc{d005115c-7ca6-4360-9683-e729f5f5957f,
  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.},
  author       = {Lagerholm, M and Peterson, Carsten and Braccini, G. and Edenbrandt, Lars and Sörnmo, Leif},
  issn         = {0018-9294},
  language     = {eng},
  number       = {7},
  pages        = {838--848},
  publisher    = {ARRAY(0xaa3ad80)},
  series       = {IEEE Transactions on Biomedical Engineering},
  title        = {Clustering ECG complexes using Hermite functions and self-organizing maps},
  url          = {http://dx.doi.org/10.1109/10.846677},
  volume       = {47},
  year         = {2000},
}