The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals
Abstract
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.
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Cites
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Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,007
Automatic Classification of Heartbeats Using ECG Morphology and Heartbeat Interval Features
deChazal, P., O'Dwyer, M., & Reilly, R. B. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827359 · Cited by 1,671
Probability Estimates for Multi-class Classification by Pairwise Coupling
Wu, T., Lin, C., & Weng, R. C. (2004) · Journal of Machine Learning Research · doi:10.5555/1005332.1016791 · Cited by 1,468
Probability Estimates for Multi-class Classification by Pairwise Coupling
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ECG beat detection using filter banks
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Cited by
A deep learning approach for ECG-based heartbeat classification for arrhythmia detection
Sannino, G., & De Pietro, G. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.03.057 · Cited by 445
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
Towards End-to-End ECG Classification With Raw Signal Extraction and Deep Neural Networks
Xu, S. S., Mak, M.-W., & Cheung, C.-C. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2871510 · Cited by 253
Satija, U., Ramkumar, B., & Manikandan, M. S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2877055 · Cited by 102
Ensemble Deep Learning for Biomedical Time Series Classification
Jin, L.-P., & Dong, J. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/6212684 · Cited by 60
Metadata
- Authors
- Ye, C., Kumar, B. V. K. V., & Coimbra, M. T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2012-10
- Volume
- 59
- Issue
- 10
- Pages
- 2930-2941
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1988183757
- Cited by
- 522
- References
- 29
- FWCI
- 11.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ye, C., Kumar, B. V. K. V., & Coimbra, M. T. (2012). Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2012.2213253
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