The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification
Abstract
This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.
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Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572
Cited by
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 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
Heartbeat classification using disease-specific feature selection
Zhang, Z., Dong, J., Luo, X., Choi, K.-S., & Wu, X. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.11.019 · Cited by 358
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
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
Alfaras, M., Soriano, M. C., & Ortín, S. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00103 · Cited by 265
Metadata
- Authors
- de Lannoy, G., Francois, D., Delbeke, J., & Verleysen, M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2012-01
- Volume
- 59
- Issue
- 1
- Pages
- 241-247
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1996894363
- Cited by
- 205
- References
- 3
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
