Estimation of Cardiovascular Risk Predictors From Non-Invasively Measured Diametric Pulse Volume Waveforms via Multiple Measurement Information Fusion
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This paper presents a novel multiple measurement information fusion approach to the estimation of cardiovascular risk predictors from non-invasive pulse volume waveforms measured at the body's diametric (arm and ankle) locations. Leveraging the fact that diametric pulse volume waveforms originate from the common central pulse waveform, the approach estimates cardiovascular risk predictors in three steps by: (1) deriving lumped-parameter models of the central-diametric arterial lines from diametric pulse volume waveforms, (2) estimating central blood pressure waveform by analyzing the diametric pulse volume waveforms using the derived arterial line models, and (3) estimating cardiovascular risk predictors (including central systolic and pulse pressures, pulse pressure amplification, and pulse transit time) from the arterial line models and central blood pressure waveform in conjunction with the diametric pulse volume waveforms. Experimental results obtained from 164 human subjects with a wide blood pressure range (systolic 144 mmHg and diastolic 103 mmHg) showed that the approach could estimate cardiovascular risk predictors accurately (r ≥ 0.78). Further analysis showed that the approach outperformed a generalized transfer function regardless of the degree of pulse pressure amplification. The approach may be integrated with already available medical devices to enable convenient out-of-clinic cardiovascular risk prediction.
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The difference in parameter distributions may indicate that the proposed optimization problem suffers from overfitting.
“This could be an indication of the consideration of a complex model in the optimization problem relative to the number of data-point available. Therefore proposed optimization problem might suffer from overfitting as pointed out in (41).”
From Sparse System Identification of Leptin Dynamics in Women With Obesity (2022) · cites this paper as [41]
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- Authors
- Ghasemi, Z., Lee, J. C., Kim, C.-S., Cheng, H.-M., Sung, S.-H., Chen, C.-H., Mukkamala, R., & Hahn, J.-O.
- Affiliations
- University of Maryland, College Park (US)
- Chonnam National University (KR)
- National Yang Ming University (TW)
- Michigan State University (US)
- Corresponding author
- Hahn, Jin-Oh
- ORCID
- Venue
- Scientific Reports
- Published
- 2018-07-11
- Volume
- 8
- Issue
- 1
- Pages
- 10433-10433
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6041350
- OpenAlex
- W2833408626
- Cited by
- 30
- References
- 47
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 3 · 2021: 6 · 2020: 9 · 2019: 4 · 2018: 1
- FWCI
- 2.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Science Foundation (1403004, 1404436)
- 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 › Surgery › Hemodynamic Monitoring and Therapy
- Keywords
- cardiovascular risk prediction, data fusion, pulse pressure amplification, pulse transit time, systolic blood pressure, generalized transfer function, non-invasive blood pressure measurement
- MeSH terms
- Aged, Blood Pressure Determination, Cardiovascular Diseases, Equipment and Supplies, Female, Humans, Male, Middle Aged, Outpatients, Risk Assessment, Pulse Wave Analysis
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