The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Heartbeat Time Series Classification With Support Vector Machines
Abstract
In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.
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Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath, S., Ulloa Cerna, A. E., Jing, L., vanMaanen, D. P., Stough, J., Hartzel, D. N., Leader, J. B., Kirchner, H. L., Stumpe, M. C., Hafez, A., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0870-z · Cited by 314
Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction
Sharma, L. N., Tripathy, R. K., & Dandapat, S. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2405134 · Cited by 296
Zheng, Y., Liu, Q., Chen, E., Ge, Y., & Zhao, J. L. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-015-4478-2 · Cited by 273
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Metadata
- Authors
- Kampouraki, A., Manis, G., & Nikou, C.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2009-07
- Volume
- 13
- Issue
- 4
- Pages
- 512-518
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2020571458
- Cited by
- 254
- References
- 36
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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