A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters
Abstract
The assessment of signal quality has been a research topic since the late 1970s, as it is mainly related to the problem of false alarms in bedside monitors in the intensive care unit (ICU), the incidence of which can be as high as 90 %, leading to alarm fatigue and a drop in the overall level of nurses and clinicians attention. The development of efficient algorithms for the quality control of long diagnostic electrocardiographic (ECG) recordings, both single- and multi-lead, and of the arterial blood pressure (ABP) signal is therefore essential for the enhancement of care quality. The ECG signal is often corrupted by noise, which can be within the frequency band of interest and can manifest similar morphologies as the ECG itself. Similarly to ECG, also the ABP signal is often corrupted by non-Gaussian, nonlinear and non-stationary noise and artifacts, especially in ICU recordings. Moreover, the reliability of several important parameters derived from ABP such as systolic blood pressure or pulse pressure is strongly affected by the quality of the ABP waveform. In this work, several up-to-date algorithms for the quality scoring of a single- or multi-lead ECG recording, based on time-domain approaches, frequency-domain approaches or a combination of the two will be reviewed, as well as methods for the quality assessment of ABP. Additionally, algorithms exploiting the relationship between ECG and pulsatile signals, such as ABP and photoplethysmographic recordings, for the reduction in the false alarm rate will be presented. Finally, some considerations will be drawn taking into account the large heterogeneity of clinical settings, applications and goals that the reviewed algorithms have to deal with.
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?
Heterogeneity in the QRS complex often poses challenges to identify the RR interval.
“Secondly, heterogeneity in the QRS complex often poses challenges to identify the RR interval.89”
From Wearable sensors for monitoring the physiological and biochemical profile of the athlete (2019) · cites this paper as [89]
Cites
Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database
Hamilton, P. S., & Tompkins, W. J. (1986) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.1986.325695 · Cited by 1,147 · see citing works
Li, Q., Mark, R. G., & Clifford, G. D. (2007) · Physiological Measurement · doi:10.1088/0967-3334/29/1/002 · Cited by 427 · see citing works
Lawless (1994) · doi:10.1097/00003246-199406000-00017 · Cited by 406 · see citing works
Poor prognosis for existing monitors in the intensive care unit
Tsien, C. L., & Fackler, J. C. (1997) · Critical Care Medicine · doi:10.1097/00003246-199704000-00010 · Cited by 309 · see citing works
Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms
Clifford, G. D., Behar, J. A., Li, Q., & Rezek, I. A. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1419 · Cited by 295 · see citing works
ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction
Behar, J. A., Oster, J., Li, Q., & Clifford, G. D. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2240452 · Cited by 294 · see citing works
A robust open-source algorithm to detect onset and duration of QRS complexes
Zong, W., Moody, G. B., & Jiang, D. (2003) · Computers in cardiology · doi:10.1109/cic.2003.1291261 · Cited by 212 · see citing works
Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform
Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.03.003 · Cited by 204 · see citing works
Alarms in the intensive care unit: how can the number of false alarms be reduced?
Chambrin, M.-C. (2001) · Critical Care · doi:10.1186/cc1021 · Cited by 176 · see citing works
Signal quality and data fusion for false alarm reduction in the intensive care unit
Li, Q., & Clifford, G. D. (2012) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2012.07.015 · Cited by 127 · see citing works
QRS detection based ECG quality assessment
Hayn, D., Jammerbund, B., & Schreier, G. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1449 · Cited by 106 · see citing works
Zong, W., Moody, G. B., & Mark, R. G. (2004) · Medical & Biological Engineering & Computing · doi:10.1007/bf02347553 · Cited by 106 · see citing works
Multimodal heart beat detection using signal quality indices
Johnson, A. E. W., Behar, J. A., Andreotti, F., Clifford, G. D., & Oster, J. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/8/1665 · Cited by 97 · see citing works
Siebig, S., Kuhls, S., Imhoff, M., Langgartner, J., Reng, M., Schölmerich, J., Ursula Gather, C., & Wrede, C. E. (2009) · Journal of Critical Care · doi:10.1016/j.jcrc.2008.09.001 · Cited by 96 · see citing works
Patient monitoring alarms in the ICU and in the operating room
Schmid, F., Goepfert, M. S., & Reuter, D. A. (2013) · Critical Care · doi:10.1186/cc12525 · Cited by 96 · see citing works
QRS morphology representation and noise estimation using the Karhunen-Loeve transform
Moody, G. B., & Mark, R. G. (2003) · Computers in cardiology · doi:10.1109/cic.1989.130540 · Cited by 95 · see citing works
Li, Q., Mark, R. G., & Clifford, G. D. (2009) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-8-13 · Cited by 92 · see citing works
Robust parameter extraction for decision support using multimodal intensive care data
Clifford, G. D., Long, W. J., Moody, G. B., & Peter Szolovits, B. (2008) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2008.0157 · Cited by 85 · see citing works
Automatic ECG quality scoring methodology: mimicking human annotators
Johannesen, L., & Galeotti, L. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1479 · Cited by 70 · see citing works
Clifford, G. D., López, D. M., Li, Q., & Rezek, I. A. (2011) · Computing in cardiology · Cited by 70 · see citing works
A signal abnormality index for arterial blood pressure waveforms
Sun, Reisner, & ', M. (2006) · Cited by 65 · see citing works
Impulse rejection filter for artifact removal in spectral analysis of biomedical signals
McNames, J., Thong, T., & Aboy, M. (2005) · doi:10.1109/iembs.2004.1403112 · Cited by 61 · see citing works
Assessing ECG signal quality on a coronary care unit
Allen, J., & Murray, A. (1996) · Physiological Measurement · doi:10.1088/0967-3334/17/4/002 · Cited by 59 · see citing works
Self-organized neural network for the quality control of 12-lead ECG signals
Chen, Y., & Yang, H. Y. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1399 · Cited by 51 · see citing works
Threshold-based system for noise detection in multilead ECG recordings
Jekova, I. I., Krasteva, V., Christov, I., et al. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1463 · Cited by 50 · see citing works
CinC challenge — Assessing the usability of ECG by ensemble decision trees
Zaunseder, S., Huhle, R., & Malberg, H. (2011) · OPUS (Augsburg University) · Cited by 46 · see citing works
Rule-based methods for ECG quality control
Moody, B. (2011) · Cited by 41 · see citing works
Pimentel, M. A. F., Santos, M., Springer, D., & Clifford, G. D. (2015) · Physiological Measurement · doi:10.1088/0967-3334/36/8/1717 · Cited by 41 · see citing works
ECG quality assessment for patient empowerment in mHealth applicationsPreprint
Hayn, Jammerbund, & Schreier, G. (2011) · Cited by 38 · see citing works
Marco, L. Y. D., Duan, W., Bojarnejad, M., et al. (2012) · Physiological Measurement · doi:10.1088/0967-3334/33/9/1435 · Cited by 36 · see citing works
Electrocardiogram signal quality assessment using an artificially reconstructed target lead
Naseri, H., & Homaeinezhad, M. R. (2014) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2013.875163 · Cited by 35 · see citing works
Data driven approach to ECG signal quality assessment using multistep SVM classification
Kužílek, J., Huptych, M., Chudáček, V., et al. (2011) · Computing in cardiology · Cited by 33 · see citing works
A new method for evaluating ECG signal quality for multi-lead arrhythmia analysis
Wang, J. Y. (2003) · Computers in cardiology · doi:10.1109/cic.2002.1166713 · Cited by 32 · see citing works
Reineck, L. A., Wallace, D. J., Barnato, A. E., & Kahn, J. M. (2013) · Critical Care · doi:10.1186/cc13033 · Cited by 31 · see citing works
Assessment of ECG quality on an Android platform
Johannesen, L. (2011) · Computing in cardiology · Cited by 30 · see citing works
A single channel ECG quality metric
Behar, J. A., Oster, J., Li, Q., & Clifford, G. D. (2012) · Computing in cardiology · Cited by 28 · see citing works
Simple scoring system for ECG quality assessment on Android platform
Chudáček, V., Zach, L., Kužílek, J., et al. (2011) · Computing in cardiology · Cited by 27 · see citing works
A Robust Approach Toward Recognizing Valid Arterial-Blood-Pressure Pulses
Asgari, S., Bergsneider, M., & Hu, X. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2034845 · Cited by 26 · see citing works
Identifying artifacts in arterial blood pressure using morphogram variability
Ali, W., Eshelman, L. J., & Saeed, M. (2005) · Computers in cardiology · doi:10.1109/cic.2004.1443034 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wearable sensors for monitoring the physiological and biochemical profile of the athlete
Seshadri, D. R., Li, R. T., Voos, J. E., Rowbottom, J. R., Alfes, C. M., Zorman, C. A., & Drummond, C. K. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0150-9 · Cited by 537 · see citing works
Can Wearable Devices Accurately Measure Heart Rate Variability? A Systematic Review
Georgiou, K., Larentzakis, A. V., Khamis, N. N., Alsuhaibani, G. I., Alaska, Y. A., & Giallafos, E. J. (2018) · Folia Medica · doi:10.2478/folmed-2018-0012 · Cited by 244 · see citing works
- Authors
- Gambarotta, N., Aletti, F., Baselli, G., & Ferrario, M.
- Affiliations
- Politecnico di Milano (IT)
- Corresponding author
- Ferrario, Manuela
- Venue
- Medical & Biological Engineering & Computing
- Published
- 2016-02-23
- Volume
- 54
- Issue
- 7
- Pages
- 1025-1035
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- OpenAlex
- W2282109974
- Cited by
- 45
- References
- 39
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 7 · 2020: 2 · 2019: 7 · 2018: 11 · 2017: 4 · 2016: 2
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Medical & Biological Engineering & Computing (publishedVersion)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- signal quality assessment, false alarm reduction, alarm fatigue, intensive care monitoring, ECG artifacts, photoplethysmography, pulse pressure, systolic blood pressure, non-stationary noise
- MeSH terms
- Algorithms, Blood Pressure, Blood Pressure Determination, Electrocardiography, Heart Rate, Humans, Quality Control, Signal Processing, Computer-Assisted, Reproducibility of Results
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
