Clustering ECG complexes using Hermite functions and self-organizing maps
Author
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.
Department/s
Publishing year
2000
Language
English
Pages
838-848
Publication/Series
IEEE Transactions on Biomedical Engineering
Volume
47
Issue
7
Document type
Journal article
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Medical Engineering
Status
Published
Research group
- Nuclear medicine, Malmö
ISBN/ISSN/Other
- ISSN: 1558-2531