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Clustering ECG complexes using Hermite functions and self-organizing maps
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 NN's 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.
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Cites
Self-organized formation of topologically correct feature maps
Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529
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Machine learning in cardiovascular medicine: are we there yet?
Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart · doi:10.1136/heartjnl-2017-311198 · Cited by 555
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
Ye, C., Kumar, B. V. K. V., & Coimbra, M. T. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2213253 · Cited by 522
A novel application of deep learning for single-lead ECG classification
Mathews, S. M., Kambhamettu, C., & Barner, K. E. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.013 · Cited by 322
Metadata
- Authors
- Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., & Sornmo, L.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2000-07
- Volume
- 47
- Issue
- 7
- Pages
- 838-848
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2017487767
- Cited by
- 547
- References
- 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., & Sornmo, L. (2000). Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.846677
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
