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Estimation of aorti...
Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning
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- Hellqvist, Henrik (författare)
- Karolinska Institutet
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- Karlsson, Mikael, Docent (författare)
- KTH,Teknisk mekanik
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- Hoffman, Johan, 1974- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Kahan, Thomas (författare)
- Karolinska Institutet
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- Spaak, Jonas (författare)
- Karolinska Institutet
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(creator_code:org_t)
- Frontiers Media SA, 2024
- 2024
- Engelska.
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Ingår i: Frontiers in Cardiovascular Medicine. - : Frontiers Media SA. - 2297-055X. ; 11
- Relaterad länk:
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https://doi.org/10.3...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- arterial stiffness
- machine learning
- photoplethysmography
- prediction models
- pulse wave analysis
- pulse wave velocity wearables
- vascular ageing
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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