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Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
KTH, School of Engineering Sciences (SCI), Engineering Mechanics.ORCID iD: 0000-0003-3869-9729
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).ORCID iD: 0000-0003-4256-0463
Division of Cardiovascular Medicine, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
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2024 (English)In: Frontiers in Cardiovascular Medicine, E-ISSN 2297-055X, Vol. 11, article id 1350726Article in journal (Refereed) Published
Abstract [en]

Introduction: Aortic stiffness plays a critical role in the evolution of cardiovascular diseases, but the assessment requires specialized equipment. Photoplethysmography (PPG) and single-lead electrocardiogram (ECG) are readily available in healthcare and wearable devices. We studied whether a brief PPG registration, alone or in combination with single-lead ECG, could be used to reliably estimate aortic stiffness. Methods: A proof-of-concept study with simultaneous high-resolution index finger recordings of infrared PPG, single-lead ECG, and finger blood pressure (Finapres) was performed in 33 participants [median age 44 (range 21–66) years, 19 men] and repeated within 2 weeks. Carotid–femoral pulse wave velocity (cfPWV; two-site tonometry with SphygmoCor) was used as a reference. A brachial single-cuff oscillometric device assessed aortic pulse wave velocity (aoPWV; Arteriograph) for further comparisons. We extracted 136 established PPG waveform features and engineered 13 new with improved coupling to the finger blood pressure curve. Height-normalized pulse arrival time (NPAT) was derived using ECG. Machine learning methods were used to develop prediction models. Results: The best PPG-based models predicted cfPWV and aoPWV well (root-mean-square errors of 0.70 and 0.52 m/s, respectively), with minor improvements by adding NPAT. Repeatability and agreement were on par with the reference equipment. A new PPG feature, an amplitude ratio from the early phase of the waveform, was most important in modelling, showing strong correlations with cfPWV and aoPWV (r = −0.81 and −0.75, respectively, both P < 0.001). Conclusion: Using new features and machine learning methods, a brief finger PPG registration can estimate aortic stiffness without requiring additional information on age, anthropometry, or blood pressure. Repeatability and agreement were comparable to those obtained using non-invasive reference equipment. Provided further validation, this readily available simple method could improve cardiovascular risk evaluation, treatment, and prognosis.

Place, publisher, year, edition, pages
Frontiers Media SA , 2024. Vol. 11, article id 1350726
Keywords [en]
arterial stiffness, machine learning, photoplethysmography, prediction models, pulse wave analysis, pulse wave velocity wearables, vascular ageing
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:kth:diva-344935DOI: 10.3389/fcvm.2024.1350726ISI: 001189887100001Scopus ID: 2-s2.0-85188422487OAI: oai:DiVA.org:kth-344935DiVA, id: diva2:1848561
Note

QC 20240404

Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-04-04Bibliographically approved

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Karlsson, MikaelHoffman, Johan

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