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Clustering ECG complexes using Hermite functions and self-organizing maps

In English

Författare

Summary, in English

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.

Publiceringsår

2000

Språk

Engelska

Sidor

838-848

Publikation/Tidskrift/Serie

IEEE Transactions on Biomedical Engineering

Volym

47

Issue

7

Dokumenttyp

Artikel i tidskrift

Förlag

IEEE - Institute of Electrical and Electronics Engineers Inc.

Ämne

  • Medical Engineering

Status

Published

Forskningsgrupp

  • Nuclear medicine, Malmö

ISBN/ISSN/Övrigt

  • ISSN: 1558-2531