The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation
Abstract
Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“Out of several pulse-to-pulse SQIs [10,[49][50][51], only the one in [10] has been tested in long-term wrist PPGs acquired outside the laboratory setting and was therefore implemented in this study.”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [50]
In the citing paper
“Karlen et al. created an algorithm with 96.21% sensitivity and 99.2% positive predictive value for good pulses using repeated Gaussian filters and cross-correlation [168].”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [168]
In the citing paper
“With this algorithm, Karlen et al. were able to distinguish where motion artifacts occurred in a beat-to-beat manner.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [168]
Citations
Cites
Lima, A. P., Beelen, P., & Bakker, J. (2002) · Critical Care Medicine · doi:10.1097/00003246-200206000-00006 · Cited by 479
Motion Artifact Reduction in Photoplethysmography Using Independent Component Analysis
Kim, B., & Yoo, S. K. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.869784 · Cited by 400
The peripheral pulse wave: Information overlooked
Murray, W. B., & Foster, P. A. (1996) · The Journal of Clinical Monitoring · doi:10.1007/bf02077634 · Cited by 231
An Automatic Beat Detection Algorithm for Pressure Signals
Aboy, M., McNames, J., Thong, T., Tsunami, D., Ellenby, M. S., & Goldstein, B. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.855725 · Cited by 216
Signal processing methods for pulse oximetry
Rusch, T. L., Sankar, R. T., & Scharf, J. E. (1996) · Computers in Biology and Medicine · doi:10.1016/0010-4825(95)00049-6 · Cited by 185
The Effect of Motion on Pulse Oximetry and Its Clinical Significance
Petterson, M. T., Begnoche, V. L., & Graybeal, J. M. (2007) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000278134.47777.a5 · Cited by 177
Signal quality measures for pulse oximetry through waveform morphology analysis
Sukor, J. A., Redmond, S. J., & Lovell, N. H. (2011) · Physiological Measurement · doi:10.1088/0967-3334/32/3/008 · Cited by 166
Lee, B., Han, J., Baek, H. J., Shin, J. H., Park, K. S., & Yi, W. J. (2010) · Physiological Measurement · doi:10.1088/0967-3334/31/12/003 · Cited by 155
CapnoBase: Signal database and tools to collect, share and annotate respiratory signals
Karlen, W., Turner, M., Cooke, E., Dumont, G. A., & Ansermino, J. M. (2010) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000087887 · Cited by 114
Kalman Filter Based Adaptive Reduction Of Motion Artifact From Photoplethysmographic Signal
Seyedtabaii, S., & Seyedtabaii, L. (2008) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1333655 · Cited by 59
Wood, L. B., & Asada, H. H. (2006) · 2006 International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2006.260359 · Cited by 39
An Improved Pre-processing Approach for Photoplethysmographic Signal
Weng, J., Ye, Z., & Weng, J. (2005) · doi:10.1109/iembs.2005.1616337 · Cited by 29
PPG delineator for real-time ubiquitous applications
Farooq, U., Jang, D.-G., Park, J.-H., & Park, S.-H. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5626023 · Cited by 25
Analysis of the photoplethysmographic signal by means of the decomposition in principal components
quez, R. H. E. I., Castellanos, M. S., guez, J. F. O. N. R. I., & Hernández-Cáceres, J. L. (2002) · Physiological Measurement · doi:10.1088/0967-3334/23/3/402 · Cited by 22
Krishnan, R., Natarajan, B., & Warren, S. (2008) · Proceedings/Proceedings - International Conference on Computer Communications and Networks · doi:10.1109/icccn.2008.ecp.71 · Cited by 19
Biosignal quality detection: An essential feature for unsupervised telehealth applications
Lovell, N. H., Redmond, S. J., Basilakis, J., & Celler, B. G. (2010) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2010.5556528 · Cited by 13
Extraction of respiratory activity from PPG and BP signals using Principal Component Analysis
Madhav, K. V., Ram, M. R., Krishna, E. H., Reddy, K. N., & Reddy, K. A. (2011) · International Conference on Communications and Signal Processing (ICCSP) · doi:10.1109/iccsp.2011.5739359 · Cited by 7
Photoplethysmogram Processing Using an Adaptive Single Frequency Phase Vocoder Algorithm
Karlen, W., Petersen, C., GOW, J. L., Ansermino, J. M., & Dumont, G. A. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-29752-6_3 · Cited by 4
Research works printed but not linked: 9
- 2003, ANSI/AAMI EC57
- Farooq U Jang D G Park J H Park S H, 2010, Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, 4582
- Karlen W Petersen C Gow J Ansermino J M Dumont G A Fred A Filipe J Gamboa H, 2012, BIOSTEC 2011: 4th Int. Joint Conf. on Biomedical Engineering Systems and Technologies, 31
- Karlen W Turner M Cooke E Dumont G A Ansermino J M, 2010, STA: Annu. Meet. of the Society for Technology in Anesthesia, 25
- Lovell N H Redmond S J Basilakis J Celler B G, 2010, 12th IEEE Int. Conf. on e-Health Networking Applications and Services (Healthcom), 81
- Madhav K V Ram M R Krishna E H Reddy K N Reddy K A, 2011, Int. Conf. on Communications and Signal Processing (ICCSP 2011), 452
- Seyedtabaii S, 2008, World Acad. Sci. Eng. Technol., 37, 173
- Weng J Ye Z Weng J, 2005, 27th Annu. Int. Conf. IEEE Engineering in Medicine and Biology Society, 41
- Wood L B Asada H H, 2006, 28th Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, 3525
Cited by
Sorted by citations
Photoplethysmogram Analysis and Applications: An Integrative Review
Park, J., Seok, H. S., Kim, S.-S., & Shin, H. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.808451 · Cited by 514
Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring
Fine, J., Branan, K. L., Rodriguez, A. J., Boonya-ananta, T., Ajmal, Ramella-Roman, J. C., McShane, M. J., & Coté, G. L. (2021) · Biosensors · doi:10.3390/bios11040126 · Cited by 348
Wearable Photoplethysmography for Cardiovascular Monitoring
Charlton, P. H., Kyriacou, P. A., Mant, J., Marozas, V., Chowienczyk, P., & Alastruey, J. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3149785 · Cited by 250
Mejía‐Mejía, E., Budidha, K., Abay, T. Y., May, J. M., & Kyriacou, P. A. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.00779 · Cited by 178
Assessing mental stress from the photoplethysmogram: a numerical study
Charlton, P. H., Celka, P., Farukh, B., Chowienczyk, P., & Alastruey, J. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aabe6a · Cited by 131
Metadata
- Authors
- Karlen, W., Kobayashi, K., Ansermino, J. M., & Dumont, G. A.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Karlen, W
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2012-09-18
- Volume
- 33
- Issue
- 10
- Pages
- 1617-1629
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W2160347936
- Cited by
- 123
- References
- 18
- Citations by year
- 2026: 10 · 2025: 8 · 2024: 7 · 2023: 10 · 2022: 12 · 2021: 11 · 2020: 10 · 2019: 11 · 2018: 13 · 2017: 7 · 2016: 9 · 2015: 6 · 2014: 6 · 2013: 3
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Hirosaki University
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmogram, pulse oximetry, signal quality estimation, pulse detection, Gaussian filtering, cross correlation function, signal artifacts, real-time monitoring, smart alarms, heart rate monitoring
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Child, Child, Preschool, Humans, Infant, Middle Aged, Quality Control, Signal Processing, Computer-Assisted, Normal Distribution, Databases, Factual, Photoplethysmography, Young Adult
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
