The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
The principles of software QRS detection
Abstract
The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.
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
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Neural networks for pattern recognition
(1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,538
Haykin, S. (1986) · Cited by 12,603
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Rogers, S. (1996) · Control Engineering Practice · doi:10.1016/0967-0661(96)82838-3 · Cited by 9,278
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
Strang, G., & Nguyen, T. Q. (1996) · Cited by 4,180
Singularity detection and processing with wavelets
Mallat, S., & Hwang, W. L. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.119727 · Cited by 3,863
Adaptive filter theory (2nd ed.)
Haykin, S. (1991) · Prentice-Hall, Inc eBooks · Cited by 1,892
Cited by
Wavelet transforms and the ECG: a review
Addison, P. S. (2005) · Physiological Measurement · doi:10.1088/0967-3334/26/5/r01 · Cited by 908
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
ECG signal analysis through hidden Markov models
Andreao, R. V., Dorizzi, B., & Boudy, J. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.877103 · Cited by 293
Current methods in electrocardiogram characterization
Martis, R. J., Acharya, U. R., & Adeli, H. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.02.012 · Cited by 278
A real-time QRS detection method based on moving-averaging incorporating with wavelet denoising
Chen, S.-W., Chen, H.-C., & Chan, H.-L. (2006) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2005.11.012 · Cited by 264
Metadata
- Authors
- Kohler, B.-U., Hennig, C., & Orglmeister, R.
- Venue
- IEEE Engineering in Medicine and Biology Magazine
- Published
- 2002
- Volume
- 21
- Issue
- 1
- Pages
- 42-57
- ISSN
- 0739-5175 (ISSN-L)
- OpenAlex
- W2099619765
- Cited by
- 1,242
- References
- 59
- FWCI
- 13.49
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The principles of software QRS detection. https://app.cochat.ai/papers/10.1109/51.993193
- The paper
- Kohler, B.-U., Hennig, C., & Orglmeister, R. (2002). The principles of software QRS detection. IEEE Engineering in Medicine and Biology Magazine. https://doi.org/10.1109/51.993193
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.
