The DOI matches its Crossref record; 29 of the 31 references it cites were found in a registry or an index.
Dynamic time warping and machine learning for signal quality assessment of pulsatile signals
Open accessAbstract
In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.
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“Some other examples are mentioned here: Bayesian network for classifying stress level [26,39] or for predicting patient's state [72], Fuzzy state machine for estimating the health status to provide alerts [68], logistic regression models [42] and association rules [41] to predict stress level and hospitalization of hemodialysis patients, respectively.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [72]
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“Several approaches have been proposed, including statistical analysis of pulse wave shape [56], assessing the level of perfusion through the perfusion index (the ratio of the amplitude of the pulsatile component of the PPG to its baseline) [56], assessing the similarity of successive pulse wave shapes using template matching [86] or dynamic time warping [87], and deep learning [88].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [87]
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Cites
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
Faires, J. D. (1978) · Cited by 6,711
The Levenberg-Marquardt algorithm: Implementation and theory
Morè, J. J. (1978) · Lecture notes in mathematics · doi:10.1007/bfb0067700 · Cited by 5,796
Photoplethysmography and its application in clinical physiological measurement
Allen, J. (2007) · Physiological Measurement · doi:10.1088/0967-3334/28/3/r01 · Cited by 3,967
Exact indexing of dynamic time warping
Keogh, E., & Ratanamahatana, C. A. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-004-0154-9 · Cited by 1,962
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
An open-source algorithm to detect onset of arterial blood pressure pulses
Zong, W., Heldt, T., Moody, G. B., & Mark, R. G. (2003) · Computers in cardiology · doi:10.1109/cic.2003.1291140 · Cited by 326
Saeed, M., Lieu, C., RABER, G., & Mark, R. G. (2003) · Computers in cardiology · doi:10.1109/cic.2002.1166854 · Cited by 273
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
Signal processing methods for heart rate variability
Clifford, G. D. (2002) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.5287/ora-noa0mqzyn · Cited by 131
Self-citation
Research works printed but not linked: 14
- Addison P S Watson J N, 2010
- Clifford G D, 2011, Comput. Cardiol., 38, 285
- Graybeal J M Petterson M T, 2004, Proc. 26th Annu. Int. Conf. IEEE EMBS, 5363
- Huang B Kinsner W, 2002, Proc. 2002 IEEE Canadian Conf. on Electrical and Computer Engineering, 1105
- Krishnan R Natarajan B Warren S, 2008, Proc. 17th Int. Conf. on Computer Communications and Networks ICCCN '08, 1
- Krishnan R Natarajan B Warren S, 2008, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 613
- Lee C M Zhang Y T, 2003, IEEE EMBS Asian-Pacific Conf. on Biomed. Eng., 194
- Lee H W, 2007, Int. J. Control Autom. Syst., 5, 701
- Reddy K A Kumar V J, 2007, Proc. IEEE Instrumentation and Measurement Technology Conf. IMTC, 1
- Relente A R Sison L G, 2002, Proc. 2nd Joint EMBS/BMES Conf., 1769
- Sukanesh R, 2010, Biomed. Softw. Comput. Hum. Sci., 16, 135
- Vullings H J L M Verhaegen M H G Verbruggen H B, 1998, Proc. 20th Annu. Conf. IEEE EMBS, 163
- Yao J Warren S, 2005, IEEE-EMBS 27th Annu. Int. Conf. of the Engineering in Medicine and Biology Society, 3585
- Zong W, 2003, Comput. Cardiol., 30, 259
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Chierico, P., Suárez, D., Fácila, L., Hornero, F., Alcaraz, R., & Rieta, J. J. (2026) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-24045-3_19
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Metadata
- Authors
- Li, Q., & Clifford, G. D.
- Affiliations
- Shandong University (CN)
- University of Oxford (GB)
- ORCID
- Venue
- Physiological Measurement
- Publisher
- IOP Publishing
- Published
- 2012-08-17
- Volume
- 33
- Issue
- 9
- Pages
- 1491-1501
- ISSN
- 0967-3334 (ISSN-L)
- 1361-6579
- OpenAlex
- W1989977532
- Cited by
- 274
- References
- 31
- Citations by year
- 2026: 18 · 2025: 13 · 2024: 17 · 2023: 26 · 2022: 28 · 2021: 25 · 2020: 22 · 2019: 18 · 2018: 25 · 2017: 20 · 2016: 25 · 2015: 15 · 2014: 9 · 2013: 8 · 2012: 5
- FWCI
- 6.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physiological Measurement (publishedVersion)
- PubMed (publishedVersion)
- 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, signal quality assessment, dynamic time warping, template matching, multilayer perceptron, arrhythmia detection, intensive care monitoring
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Quality Control, Time Factors, Photoplethysmography
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