The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Support Vector Machine-Based Expert System for Reliable Heartbeat Recognition
Abstract
This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
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
ECG Classification Using Wavelet Packet Entropy and Random Forests
Li, T., & Zhou, M. (2016) · Entropy · doi:10.3390/e18080285 · Cited by 478
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
Convolutional Neural Networks for patient-specific ECG classification
Kiranyaz, S., Ince, T., Hamila, R., & Gabbouj, M. (2015) · 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318926 · Cited by 309
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
Metadata
- Authors
- Osowski, S., Hoai, L. T., & Markiewicz, T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-04
- Volume
- 51
- Issue
- 4
- Pages
- 582-589
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2100537461
- Cited by
- 536
- References
- 1
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- closed
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