The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Automated identification of normal and diabetes heart rate signals using nonlinear measures
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
A Real-Time QRS Detection Algorithm
Pan, J., & Tompkins, W. J. (1985) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1985.325532 · Cited by 7,855
Approximate entropy as a measure of system complexity.
Pincus, S. M. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.6.2297 · Cited by 5,811
Heart rate variability: A review
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C. M., & Suri, J. S. (2006) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-006-0119-0 · Cited by 2,820
Self-citationAn Introduction to Computational Learning Theory
Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697
Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes
and the WHO Multinational Study Group, Morrish, N. J., Wang, S.-L., Stevens, L. K., Fuller, J. H., & Keen, H. (2001) · Diabetologia · doi:10.1007/pl00002934 · Cited by 1,288
Fractal mechanisms and heart rate dynamics
Peng, C.-K., Havlin, S., Hausdorff, J. M., Mietus, J. E., Stanley, H. E., & Goldberger, A. L. (1995) · Journal of Electrocardiology · doi:10.1016/s0022-0736(95)80017-4 · Cited by 358
Cited by
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., & Yarifard, A. A. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.01.004 · Cited by 499
Sharma, R., Pachori, R., & Acharya, U. (2015) · Entropy · doi:10.3390/e17020669 · Cited by 305
Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm
Maniruzzaman, M., Kumar, N., Menhazul Abedin, M., Shaykhul Islam, M., Suri, H. S., El-Baz, A. S., & Suri, J. S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.09.004 · Cited by 270
Self-citationDiabetes Detection and Sensor-Based Continuous Glucose Monitoring – A Deep Learning Approach
Swapna, G., & Soman, K. P. (2021) · Internet of Things · doi:10.1007/978-3-030-66633-0_11 · Cited by 9
Metadata
- Authors
- Rajendra Acharya, U., Faust, O., Adib Kadri, N., Suri, J. S., & Yu, W.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 43
- Issue
- 10
- Pages
- 1523-1529
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2116570678
- Cited by
- 163
- References
- 9
- FWCI
- 6.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Rajendra Acharya, U., Faust, O., Adib Kadri, N., Suri, J. S., & Yu, W. (2013). Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in Biology and Medicine. https://doi.org/10.1016/j.compbiomed.2013.05.024
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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.
