The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Optimization of ECG Classification by Means of Feature Selection
Open accessAbstract
This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57:1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring
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Cites
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
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Wearable Medical Systems for p-Health
Teng, X.-F., Zhang, Y.-T., Poon, C. C. Y., & Bonato, P. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008248 · Cited by 310
Cited by
Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks
Kiranyaz, S., Ince, T., & Gabbouj, M. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2468589 · Cited by 1,912
ECG Classification Using Wavelet Packet Entropy and Random Forests
Li, T., & Zhou, M. (2016) · Entropy · doi:10.3390/e18080285 · Cited by 478
Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network
Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., & Ou, C. (2021) · Entropy · doi:10.3390/e23010119 · Cited by 308
Ansari, Y., Mourad, O., Qaraqe, K., & Serpedin, E. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1246746 · Cited by 291
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
- Mar, T., Zaunseder, S., Martínez, J. P., Llamedo, M., & Poll, R.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2011-08
- Volume
- 58
- Issue
- 8
- Pages
- 2168-2177
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2133876672
- Cited by
- 324
- References
- 3
- FWCI
- 5.33
- Type
- journal article
- Language
- en
- Open access
- green
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