A comparison of the noise sensitivity of nine QRS detection algorithms
Abstract
The noise sensitivities for nine different QRS detection algorithms were measured for a normal, single-channel lead II, synthesized ECG corrupted with five different types of synthesized noise. The noise types were electromyographic interference, 60 Hz powerline interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite noise corrupted data.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
3-12 Hz models the motion artifact noises attributable to tremors.
“3–12 Hz models the motion artifact noises attributable to tremors, which occur within this frequency range20,21.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [21]
50-100 Hz reflects consistent electrode contact noise.
“The 50–100 Hz domain reflects consistent electrode contact noise21”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [21]
100-150 Hz contains higher-frequency muscle noises.
“the 12–50 Hz and 100–150 Hz ranges contain the lower and higher-frequency muscle noises, respectively17,21.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [21]
The MIT-BIH dataset noises represent three types of noises frequently encountered in ECGs: baseline wander, muscle artifact, and electrode motion artifact.
“The MIT-BIH dataset noises, each obtained at a sampling frequency of 360 Hz, represent three types of noises frequently encountered in ECGs: baseline wander noise, a low-frequency noise produced by lead or subject movement17, muscle artifact, caused by muscle contractions21, and electrode motion artifact, which is caused by irregular movement of the electrodes during ECG recordings17,21.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [21]
12-50 Hz contains lower-frequency muscle noises.
“the 12–50 Hz and 100–150 Hz ranges contain the lower and higher-frequency muscle noises, respectively17,21.”
From Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices (2023) · cites this paper as [21]
Cited by
Sorted by citations
Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use
Electrophysiology, T. F. O. T. E. S. O. C. T. N. A. (1996) · Circulation · doi:10.1161/01.cir.93.5.1043 · Cited by 17,159 · see citing works
Heart rate variability: Standards of measurement, physiological interpretation, and clinical use
Malik, M., Bigger, J. T., Camm, A. J., Kleiger, R. E., Malliani, A., Moss, A. J., & Schwartz, P. J. (1996) · European Heart Journal · doi:10.1093/oxfordjournals.eurheartj.a014868 · Cited by 15,289 · see citing works
Heart rate variability: origins, methods, and interpretive caveats
BERNTSON, G. G., THOMAS BIGGER, J., ECKBERG, D. L., GROSSMAN, P., KAUFMANN, P. G., MALIK, M., NAGARAJA, H. N., PORGES, S. W., SAUL, J. P., STONE, P. H., et al. (1997) · Psychophysiology · doi:10.1111/j.1469-8986.1997.tb02140.x · Cited by 3,962 · see citing works
Heart Rate Turbulence: Standards of Measurement, Physiological Interpretation, and Clinical Use
Bauer, A., Malik, M., Schmidt, G., Barthel, P., Bonnemeier, H., Cygankiewicz, I., Guzik, P., Lombardi, F., Müller, A., Oto, A., et al. (2008) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2008.07.041 · Cited by 462 · see citing works
A freezing‐like posture to pictures of mutilation
Azevedo, T. M., Volchan, E., Imbiriba, L. A., Rodrigues, E. C., Oliveira, J. M., Oliveira, L. F., Lutterbach, L. G., & Vargas, C. D. (2005) · Psychophysiology · doi:10.1111/j.1469-8986.2005.00287.x · Cited by 320 · see citing works
ACC/AHA guidelines for ambulatory electrocardiography
Crawford, M. H., Bernstein, S. J., Deedwania, P. C., DiMarco, J. P., Ferrick, K. J., Garson, A., Green, L. A., Greene, H. L., Silka, M. J., Stone, P. H., et al. (1999) · Journal of the American College of Cardiology · doi:10.1016/s0735-1097(99)00354-x · Cited by 309 · see citing works
Gaussian process robust regression for noisy heart rate data
Stegle, O., Fallert, S. V., MacKay, D. J. C., & Brage, S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.923118 · Cited by 213 · see citing works
Postural modulation induced by pictures depicting prosocial or dangerous contexts
Facchinetti, L. D., Imbiriba, L. A., Azevedo, T. M., Vargas, C. D., & Volchan, E. (2006) · Neuroscience Letters · doi:10.1016/j.neulet.2006.09.063 · Cited by 183 · see citing works
Israel, B., Buysse, D. J., Krafty, R. T., Begley, A., Miewald, J., & Hall, M. (2012) · Sleep · doi:10.5665/sleep.2088 · Cited by 114 · see citing works
Khunte, A., Sangha, V., Oikonomou, E. K., Dhingra, L. S., Aminorroaya, A., Mortazavi, B. J., Coppi, A., Brandt, C. A., Krumholz, H. M., & Khera, R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00869-w · Cited by 95 · see citing works
Ellis, R. J., Zhu, B., Koenig, J., Thayer, J. F., & Wang, Y. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/9/1827 · Cited by 85 · see citing works
Endogenous Circadian Rhythm in Vasovagal Response to Head-Up Tilt
Hu, K., Scheer, F. A. J. L., Laker, M., Smales, C., & Shea, S. A. (2011) · Circulation · doi:10.1161/circulationaha.110.943019 · Cited by 74 · see citing works
Zhang, Y., Lai, J., Zhao, C., Wang, J., Yan, Y., Chen, M., Ji, L., Guo, J., Han, B., Shi, Y., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102992 · Cited by 5 · see citing works
Dethier, V., & Philippot, P. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.03.013 · Cited by 1 · see citing works
- Authors
- Friesen, G. M., Jannett, T. C., Jadallah, M. A., Yates, S. L., Quint, S. R., & Nagle, H. T.
- Affiliations
- Auburn University (US)
- University of Alabama (US)
- North Carolina State University (US)
- North Carolina Agricultural and Technical State University (US)
- ORCID
- Nagle, H.T.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1990
- Volume
- 37
- Issue
- 1
- Pages
- 85-98
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2104563593
- Cited by
- 1,043
- References
- 0
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 20 · 2023: 22 · 2022: 34 · 2021: 30 · 2020: 41 · 2019: 47 · 2018: 45 · 2017: 47 · 2016: 53 · 2015: 43 · 2014: 56 · 2013: 47 · 2012: 48
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- QRS complex detection, noise sensitivity, ECG, electromyographic interference, baseline drift, respiration, baseline shift, false positives, detection delay, digital filters, algorithm comparison
- MeSH terms
- Algorithms, Computer Simulation, Electrocardiography, Models, Cardiovascular, Signal Processing, Computer-Assisted, Software
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
