The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals
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Citations
Cites
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
Numerical Recipes in C: The Art of Scientific Computing.
Birchenhall, C., Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · The Economic Journal · doi:10.2307/2234664 · Cited by 4,355
Hulley, S. B. (1997) · Cited by 1,753
(2015) · Biological Engineering · doi:10.13031/issn.1934-2799 · Cited by 274
A method for detection of atrial fibrillation using RR intervals
Tateno, K., & Glass, L. (2002) · Computers in cardiology · doi:10.1109/cic.2000.898539 · Cited by 78
Self-citationAtrial Fibrillation: Mechanisms and Management
Liu, T., Tse, G., Yu, L., Korantzopoulos, P. G., & Letsas, Κ. P. (2019) · Cardiology Research and Practice · doi:10.1155/2019/8909371 · Cited by 43
Heart rate variability preceding onset of atrial fibrillation.
Andresen, D., & Brüggemann, T. (1998) · PubMed · Cited by 33
Cited by
Automatic Real Time Detection of Atrial Fibrillation
Dash, S., Chon, K. H., Lu, S., & Raeder, E. A. (2009) · Annals of Biomedical Engineering · doi:10.1007/s10439-009-9740-z · Cited by 313
McManus, D. D., Lee, J., Maitas, O., Esa, N., Pidikiti, R., Carlucci, A., Harrington, J., Mick, E., & Chon, K. H. (2013) · Heart Rhythm · doi:10.1016/j.hrthm.2012.12.001 · Cited by 253
PULSE‐SMART: Pulse‐Based Arrhythmia Discrimination Using a Novel Smartphone Application
McMANUS, D. D., CHONG, J. W., SONI, A., SACZYNSKI, J. S., ESA, N., NAPOLITANO, C., DARLING, C. E., BOYER, E., ROSEN, R. K., FLOYD, K. C., et al. (2016) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.12842 · Cited by 156
Smart-watches: a potential challenger to the implantable loop recorder?
Carpenter, A., & Frontera, A. (2016) · Europace · doi:10.1093/europace/euv427 · Cited by 86
Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia
Cui, J., Wang, L., He, X., De Albuquerque, V. H. C., AlQahtani, S. A., & Hassan, M. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-021-06487-5 · Cited by 43
Metadata
- Authors
- Tateno, K., & Glass, L.
- Venue
- Medical and Biological Engineering and Computing
- Published
- 2001-11
- Volume
- 39
- Issue
- 6
- Pages
- 664-671
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- OpenAlex
- W2109692256
- Cited by
- 295
- References
- 7
- FWCI
- 0.45
- 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.
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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.
