Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning
© 2024 Hellqvist, Karlsson, Hoffman, Kahan and Spaak..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
|---|---|
| Enthalten in: |
Frontiers in cardiovascular medicine - 11(2024) vom: 10., Seite 1350726 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hellqvist, Henrik [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Arterial stiffness |
|---|
| Anmerkungen: |
Date Revised 27.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.3389/fcvm.2024.1350726 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370188950 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370188950 | ||
| 003 | DE-627 | ||
| 005 | 20240328000538.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240327s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.3389/fcvm.2024.1350726 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1351.xml |
| 035 | |a (DE-627)NLM370188950 | ||
| 035 | |a (NLM)38529332 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hellqvist, Henrik |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Estimation of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 27.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 Hellqvist, Karlsson, Hoffman, Kahan and Spaak. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a arterial stiffness | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a photoplethysmography | |
| 650 | 4 | |a prediction models | |
| 650 | 4 | |a pulse wave analysis | |
| 650 | 4 | |a pulse wave velocity wearables | |
| 650 | 4 | |a vascular ageing | |
| 700 | 1 | |a Karlsson, Mikael |e verfasserin |4 aut | |
| 700 | 1 | |a Hoffman, Johan |e verfasserin |4 aut | |
| 700 | 1 | |a Kahan, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Spaak, Jonas |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Frontiers in cardiovascular medicine |d 2014 |g 11(2024) vom: 10., Seite 1350726 |w (DE-627)NLM255534736 |x 2297-055X |7 nnns |
| 773 | 1 | 8 | |g volume:11 |g year:2024 |g day:10 |g pages:1350726 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.3389/fcvm.2024.1350726 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 11 |j 2024 |b 10 |h 1350726 | ||