The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach
Abstract
Correct detection and classification of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is of pivotal importance for an automatic external defibrillator and patient monitoring. In this paper, a VF/VT classification algorithm using a machine learning method, a support vector machine, is proposed. A total of 14 metrics were extracted from a specific window length of the electrocardiogram (ECG). A genetic algorithm was then used to select the optimal variable combinations. Three annotated public domain ECG databases (the American Heart Association Database, the Creighton University Ventricular Tachyarrhythmia Database, and the MIT-BIH Malignant Ventricular Arrhythmia Database) were used as training, test, and validation datasets. Different window sizes, varying from 1 to 10 s were tested. An accuracy (Ac) of 98.1%, sensitivity (Se) of 98.4%, and specificity (Sp) of 98.0% were obtained on the in-sample training data with 5 s-window size and two selected metrics. On the out-of-sample validation data, an Ac of 96.3% ± 3.4%, Se of 96.2% ± 2.7%, and Sp of 96.2% ± 4.6% were obtained by fivefold cross validation. The results surpass those of current reported methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Li, Q., Mark, R. G., & Clifford, G. D. (2008) · Physiological Measurement · doi:10.1088/0967-3334/29/1/002 · Cited by 426
Self-citationAmann, A., Tratnig, R., & Unterkofler, K. (2005) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-4-60 · Cited by 154
Evaluating arrhythmias in ECG signals using wavelet transforms
Addison, P. S., Watson, J. N., Clegg, G. R., Holzer, M. S., Sterz, F., & Robertson, C. E. (2000) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.870237 · Cited by 105
Recognition of ventricular fibrillation using neural networks
Clayton, R. H., Murray, A., & Campbell, R. W. F. (1994) · Medical & Biological Engineering & Computing · doi:10.1007/bf02518922 · Cited by 89
Cited by
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347
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
Ansari, Y., Mourad, O., Qaraqe, K., & Serpedin, E. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1246746 · Cited by 291
Applications of Artificial Intelligence in Cardiology. The Future is Already Here
Dorado-Díaz, P. I., Sampedro-Gómez, J., Vicente-Palacios, V., & Sánchez, P. L. (2019) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2019.05.014 · Cited by 111
Satija, U., Ramkumar, B., & Manikandan, M. S. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2877055 · Cited by 102
Metadata
- Authors
- Li, Q., Rajagopalan, C., & Clifford, G. D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2014-06
- Volume
- 61
- Issue
- 6
- Pages
- 1607-1613
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2000364692
- Cited by
- 274
- References
- 6
- FWCI
- 11.44
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
