The DOI matches its Crossref record; 243 of the 243 references it cites were found in a registry or an index.
Photoplethysmography signal processing and synthesis
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Integrity signals
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In the citing paper
“This low-pass filtering is a compromise between retaining the original signal features and increasing robustness to noise [10].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [10]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,707
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
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
Atrial fibrillation as an independent risk factor for stroke: the Framingham study
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991) · Stroke · doi:10.1161/01.str.22.8.983 · Cited by 7,388
An overview of heart rate variability metrics and norms
Shaffer, F., & Ginsberg, J. P. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00258 · Cited by 7,185
The impact of the MIT-BIH Arrhythmia Database
Moody, G. B., & Mark, R. G. (2001) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/51.932724 · Cited by 4,834
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
Self-citationSeymour, C. W., Liu, V. X., Iwashyna, T. J., Brunkhorst, F. M., Rea, T. D., Scherag, A., Rubenfeld, G., Kahn, J. M., Shankar-Hari, M., Singer, M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0288 · Cited by 3,939
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,359
Flagged first, then by citations
Research works printed but not linked: 2
- Elgendi, 2014, Detection of c, d, and e waves in the acceleration photoplethysmogram, Comput. Methods Programs Biomed., 117, 125, 10.1016/j.cmpb.2014.08.001 · DOI
- Sviridova, 2019, towards understanding of photoplethysmogram dynamics through nonlinear time series analysis, Seisan Kenkyu, 71, 141
Cited by
Sorted by citations
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
Self-citation
Metadata
- Authors
- Mejía-Mejía, E., Allen, J., Budidha, K., El-Hajj, C., Kyriacou, P. A., & Charlton, P. H.
- Affiliations
- Coventry University (GB)
- University of London (GB)
- Corresponding author
- Allen, John
- Budidha, Karthik
- El-Hajj, Chadi
- Kyriacou, Panicos A.
- Charlton, Peter H.
- Venue
- Photoplethysmography
- Publisher
- Elsevier
- Published
- 2022
- Pages
- 69-146
- OpenAlex
- W3186366455
- Cited by
- 138
- References
- 243
- Citations by year
- 2026: 37 · 2025: 37 · 2024: 37 · 2023: 14 · 2022: 13
- FWCI
- 35.48
- Type
- book chapter
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Elsevier eBooks (publishedVersion)
- Pure (Coventry University) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- photoplethysmography, PPG signal processing, signal preprocessing, time-domain analysis, frequency domain analysis, machine learning, physiological parameter estimation
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