The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
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
Projections of Global Mortality and Burden of Disease from 2002 to 2030
Mathers, C. D., & Loncar, D. (2006) · PLoS Medicine · doi:10.1371/journal.pmed.0030442 · Cited by 11,386
Cited by
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,647
Self-citationArrhythmia detection using deep convolutional neural network with long duration ECG signals
Yıldırım, Ö., Pławiak, P., Tan, R.-S., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 851
Self-citationAutomated EEG-based screening of depression using deep convolutional neural network
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650
Self-citationAn automated system for epilepsy detection using EEG brain signals based on deep learning approach
Ullah, I., Hussain, M., Qazi, E.-U.-H., & Aboalsamh, H. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.04.021 · Cited by 622
Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018) · Information Sciences · doi:10.1016/j.ins.2018.01.051 · Cited by 448
Self-citation
Metadata
- Authors
- Acharya, U. R., Fujita, H., Lih, O. S., Adam, M., Tan, J. H., & Chua, C. K.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 132
- Pages
- 62-71
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2620656322
- Cited by
- 373
- References
- 4
- FWCI
- 30.44
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. https://app.cochat.ai/papers/10.1016/j.knosys.2017.06.003
- The paper
- Acharya, U. R., Fujita, H., Lih, O. S., Adam, M., Tan, J. H., & Chua, C. K. (2017). Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2017.06.003
Data sources
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.
