Photoplethysmography signal analysis to assess obesity, age group and hypertension
Abstract
Photoplethysmography (PPG) provides a simple, convenient and noninvasive method to assess pulse oximetry. Several attempts have been made to use PPG also to estimate blood pressure and arterial stiffness. This paper attempts to assess obesity classes, age group, and hypertension classes using PPG measured from the finger. One set of features was derived from the normalized pulse width of PPG and the other from original PPG. The features were calculated based on the pulse decomposition analysis using five lognormal functions and the up-slope of the PPG pulse. Using kNN and SVM as classifiers, the results were validated using leave-one-out validation. Performances of both features sets have no significant difference, and the kNN outperformed the SVM. The best accuracies are 93%, 88%, and 92% for obesity (5 classes), age group (7 classes), and hypertension (4 classes) respectively. These three assessment targets have a strong relationship with arterial stiffness, therefore it also leads to a study about arterial stiffness using PPG. Width normalization to 1 second might affect some features points based on pulse decomposition analysis. This study also found that the up-slope analysis might give good indices when width normalization was employed. However, these findings still require more experiments to gain conclusions that are more comprehensive.
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Ferdinando et al. were able to identify five classes of obesity from PPG waveforms using 17 parameters derived from the decomposition of the PPG waveform into five lognormal functions.
“Using k-nearest neighbor and support vector machines, Ferdinando et al. were able to identify five classes of obesity from PPG waveforms using 17 parameters derived from the decomposition of the PPG waveform into five lognormal functions.”
From Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring (2021) · cites this paper as [76]
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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 · see citing works
- Authors
- Ferdinando, H., Huotari, M., & Myllyla, T.
- Affiliations
- Petra Christian University (ID)
- University of Oulu (FI)
- ORCID
- Ferdinando, Hany
- Venue
- 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2019-07
- Volume
- 2019
- Pages
- 5572-5575
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2979702794
- Cited by
- 9
- References
- 21
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 2 · 2021: 1 · 2020: 2
- FWCI
- 1.56
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Disease Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- photoplethysmography, obesity classification, hypertension classification, pulse decomposition, finger PPG, k-nearest neighbors, support vector machine
- MeSH terms
- Humans, Hypertension, Obesity, Oximetry, Signal Processing, Computer-Assisted, Photoplethysmography
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