Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms
One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to es...
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| Vydáno v: | PloS one Ročník 16; číslo 6; s. e0245026 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
28.06.2021
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (
e.g
. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (
https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal
). |
|---|---|
| AbstractList | One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (
e.g
. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (
https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal
). One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal). One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository ( One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal). |
| Audience | Academic |
| Author | Alastruey, Jordi Chowienczyk, Philip Jin, Weiwei |
| AuthorAffiliation | 2 Department of Clinical Pharmacology, St. Thomas’ Hospital, King’s College London, London, United Kingdom University of Torino, ITALY 3 World-Class Research Centre, Digital Biodesign and Personalized Healthcare, Sechenov University, Moscow, Russia 1 Department of Biomedical Engineering, King’s College London, London, United Kingdom |
| AuthorAffiliation_xml | – name: 1 Department of Biomedical Engineering, King’s College London, London, United Kingdom – name: University of Torino, ITALY – name: 2 Department of Clinical Pharmacology, St. Thomas’ Hospital, King’s College London, London, United Kingdom – name: 3 World-Class Research Centre, Digital Biodesign and Personalized Healthcare, Sechenov University, Moscow, Russia |
| Author_xml | – sequence: 1 givenname: Weiwei orcidid: 0000-0002-0919-2470 surname: Jin fullname: Jin, Weiwei – sequence: 2 givenname: Philip surname: Chowienczyk fullname: Chowienczyk, Philip – sequence: 3 givenname: Jordi orcidid: 0000-0003-3742-5259 surname: Alastruey fullname: Alastruey, Jordi |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34181640$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/math8050662 10.1097/00004872-200412000-00010 10.1038/s41591-018-0240-2 10.1161/HYPERTENSIONAHA.119.12655 10.1097/MBP.0b013e3283614168 10.1016/j.neuroimage.2018.06.001 10.1017/thg.2012.89 10.1161/HYPERTENSIONAHA.109.129114 10.1038/s41598-018-19457-0 10.1016/j.neunet.2014.09.003 10.1093/ajh/hpx140 10.1109/TBCAS.2019.2892297 10.1038/nrneph.2017.78 10.1152/ajpheart.00218.2019 10.1016/j.exger.2017.06.007 10.1093/eurheartj/ehl254 10.1002/ejhf.1333 10.1007/s10439-013-0854-y 10.1016/j.jacc.2009.10.061 10.1161/HYPERTENSIONAHA.109.139964 10.1097/HJH.0b013e3282f82c3e 10.1162/neco.1997.9.8.1735 10.1097/HJH.0000000000002373 10.1161/01.HYP.0000128420.01881.aa 10.1016/j.artres.2010.03.001 10.1007/s13246-019-00722-z 10.1161/CIRCRESAHA.111.246876 10.1155/2018/5767864 10.1093/ije/dyr207 10.1088/1361-6579/aabe6a 10.1097/HJH.0b013e32834fa8b0 10.1080/08037051.2017.1394791 10.1016/j.artres.2009.02.006 10.1002/ehf2.12419 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Jin et al 2021 Jin et al |
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| References | J Schmidhuber (pone.0245026.ref026) 2015; 61 A Jekell (pone.0245026.ref033) 2018; 27 C Vlachopoulos (pone.0245026.ref008) 2010; 55 LM Van Bortel (pone.0245026.ref009) 2012; 30 PH Charlton (pone.0245026.ref021) 2018; 39 LdR Mikael (pone.0245026.ref028) 2017; 109 J Niebauer (pone.0245026.ref005) 2020; 19 P Salvi (pone.0245026.ref031) 2004; 22 ZI Attia (pone.0245026.ref012) 2019; 25 SP Karunathilake (pone.0245026.ref016) 2018; 2018 PM Nilsson (pone.0245026.ref006) 2008; 26 M Cikes (pone.0245026.ref015) 2019; 21 JR Weir-McCall (pone.0245026.ref036) 2017; 17 P Segers (pone.0245026.ref035) 2009; 3 B Hametner (pone.0245026.ref032) 2013; 18 PM Nilsson (pone.0245026.ref003) 2009; 54 SE Awan (pone.0245026.ref014) 2019; 6 A Grillo (pone.0245026.ref037) 2018; 31 NK Chakshu (pone.0245026.ref017) 2020 NR Gaddum (pone.0245026.ref027) 2013; 41 S Hochreiter (pone.0245026.ref024) 1997; 9 D Biswas (pone.0245026.ref013) 2019; 13 P Tavallali (pone.0245026.ref018) 2018; 8 S Laurent (pone.0245026.ref010) 2006; 27 A Moayyeri (pone.0245026.ref020) 2013; 42 PH Charlton (pone.0245026.ref022) 2019; 317 A Laina (pone.0245026.ref001) 2018; 109 BJ North (pone.0245026.ref002) 2012; 110 M Gomez-Sanchez (pone.0245026.ref004) 2020; 38 A Moayyeri (pone.0245026.ref019) 2013; 16 GF Mitchell (pone.0245026.ref029) 2004; 43 KL Wang (pone.0245026.ref030) 2010; 55 H Perez (pone.0245026.ref023) 2020; 8 Z Cui (pone.0245026.ref038) 2018; 178 PG Shiels (pone.0245026.ref040) 2017; 13 S Laurent (pone.0245026.ref007) 2019; 74 ZC Lipton (pone.0245026.ref025) 2015 IB Wilkinson (pone.0245026.ref034) 2010; 4 MM Mukaka (pone.0245026.ref039) 2012; 24 AM Alqudah (pone.0245026.ref011) 2019; 42 |
| References_xml | – volume: 8 issue: 5 year: 2020 ident: pone.0245026.ref023 article-title: Improving the accuracy of convolutional neural networks by identifying and removing outlier images in datasets using t-SNE publication-title: Mathematics doi: 10.3390/math8050662 – volume: 22 start-page: 2285 issue: 12 year: 2004 ident: pone.0245026.ref031 article-title: Validation of a new non-invasive portable tonometer for determining arterial pressure wave and pulse wave velocity: The PulsePen device publication-title: Journal of Hypertension doi: 10.1097/00004872-200412000-00010 – volume: 25 start-page: 70 issue: 1 year: 2019 ident: pone.0245026.ref012 article-title: Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram publication-title: Nature Medicine doi: 10.1038/s41591-018-0240-2 – volume: 74 start-page: 218 issue: 2 year: 2019 ident: pone.0245026.ref007 article-title: Concept of extremes in vascular aging: From early vascular aging to supernormal vascular aging publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.119.12655 – volume: 17 start-page: 1 issue: 1 year: 2017 ident: pone.0245026.ref036 article-title: Effects of inaccuracies in arterial path length measurement on differences in MRI and tonometry measured pulse wave velocity publication-title: BMC Cardiovascular Disorders – volume: 18 start-page: 173 issue: 3 year: 2013 ident: pone.0245026.ref032 article-title: Oscillometric estimation of aortic pulse wave velocity: Comparison with intra-aortic catheter measurements publication-title: Blood Pressure Monitoring doi: 10.1097/MBP.0b013e3283614168 – volume: 178 start-page: 622 issue: May year: 2018 ident: pone.0245026.ref038 article-title: The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features publication-title: NeuroImage doi: 10.1016/j.neuroimage.2018.06.001 – volume: 16 start-page: 144 issue: 1 year: 2013 ident: pone.0245026.ref019 article-title: The UK adult twin registry (TwinsUK resource) publication-title: Twin Research and Human Genetics doi: 10.1017/thg.2012.89 – volume: 54 start-page: 3 issue: 1 year: 2009 ident: pone.0245026.ref003 article-title: Vascular aging: A tale of EVA and ADAM in cardiovascular risk assessment and prevention publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.109.129114 – volume: 8 start-page: 1 issue: 1 year: 2018 ident: pone.0245026.ref018 article-title: Artificial intelligence estimation of carotid-femoral pulse wave velocity using carotid waveform publication-title: Scientific Reports doi: 10.1038/s41598-018-19457-0 – volume: 61 start-page: 85 year: 2015 ident: pone.0245026.ref026 article-title: Deep learning in neural networks: An overview publication-title: Neural Networks doi: 10.1016/j.neunet.2014.09.003 – volume: 31 start-page: 80 issue: 1 year: 2018 ident: pone.0245026.ref037 article-title: Short-Term Repeatability of Noninvasive Aortic Pulse Wave Velocity Assessment: Comparison between Methods and Devices publication-title: American Journal of Hypertension doi: 10.1093/ajh/hpx140 – volume: 13 start-page: 282 issue: 2 year: 2019 ident: pone.0245026.ref013 article-title: CorNET: Deep learning framework for PPG-based heart rate estimation and biometric identification in ambulant environment publication-title: IEEE Transactions on Biomedical Circuits and Systems doi: 10.1109/TBCAS.2019.2892297 – volume: 13 start-page: 471 issue: 8 year: 2017 ident: pone.0245026.ref040 article-title: The role of epigenetics in renal ageing publication-title: Nature Reviews Nephrology doi: 10.1038/nrneph.2017.78 – volume: 317 start-page: H1062 issue: 5 year: 2019 ident: pone.0245026.ref022 article-title: Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes publication-title: American journal of physiology Heart and circulatory physiology doi: 10.1152/ajpheart.00218.2019 – volume: 109 start-page: 16 year: 2018 ident: pone.0245026.ref001 article-title: Vascular ageing: Underlying mechanisms and clinical implications publication-title: Experimental Gerontology doi: 10.1016/j.exger.2017.06.007 – volume: 27 start-page: 2588 issue: 21 year: 2006 ident: pone.0245026.ref010 article-title: Expert consensus document on arterial stiffness: Methodological issues and clinical applications publication-title: European Heart Journal doi: 10.1093/eurheartj/ehl254 – volume: 21 start-page: 74 issue: 1 year: 2019 ident: pone.0245026.ref015 article-title: Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy publication-title: European Journal of Heart Failure doi: 10.1002/ejhf.1333 – volume: 41 start-page: 2617 issue: 12 year: 2013 ident: pone.0245026.ref027 article-title: A technical assessment of pulse wave velocity algorithms applied to non-invasive arterial waveforms publication-title: Annals of Biomedical Engineering doi: 10.1007/s10439-013-0854-y – volume: 55 start-page: 1318 issue: 13 year: 2010 ident: pone.0245026.ref008 article-title: Prediction of cardiovascular events and all-cause mortality with arterial stiffness. A systematic review and meta-analysis publication-title: Journal of the American College of Cardiology doi: 10.1016/j.jacc.2009.10.061 – volume: 55 start-page: 799 issue: 3 year: 2010 ident: pone.0245026.ref030 article-title: Wave reflection and arterial stiffness in the prediction of 15-year all-cause and cardiovascular mortalities: A community-based study publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.109.139964 – volume: 26 start-page: 1049 issue: 6 year: 2008 ident: pone.0245026.ref006 article-title: The early life origins of vascular ageing and cardiovascular risk: The EVA syndrome publication-title: Journal of Hypertension doi: 10.1097/HJH.0b013e3282f82c3e – volume: 19 start-page: 460 issue: 3 year: 2020 ident: pone.0245026.ref005 article-title: Acute effects of winter sports and indoor cycling on arterial stiffness publication-title: Journal of Sports Science and Medicine – volume: 9 start-page: 1735 year: 1997 ident: pone.0245026.ref024 article-title: Long short-term memory publication-title: Neural Computation doi: 10.1162/neco.1997.9.8.1735 – volume: 38 start-page: 1110 issue: 6 year: 2020 ident: pone.0245026.ref004 article-title: Vascular aging and its relationship with lifestyles and other risk factors in the general Spanish population: Early Vascular Ageing Study publication-title: Journal of hypertension doi: 10.1097/HJH.0000000000002373 – volume: 43 start-page: 1239 issue: 6 year: 2004 ident: pone.0245026.ref029 article-title: Changes in arterial stiffness and wave reflection with advancing age in healthy men and women: The Framingham Heart Study publication-title: Hypertension doi: 10.1161/01.HYP.0000128420.01881.aa – year: 2020 ident: pone.0245026.ref017 article-title: Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis publication-title: Biomechanics and Modeling in Mechanobiology – volume: 4 start-page: 34 issue: 2 year: 2010 ident: pone.0245026.ref034 article-title: ARTERY Society guidelines for validation of non-invasive haemodynamic measurement devices: Part 1, arterial pulse wave velocity publication-title: Artery Research doi: 10.1016/j.artres.2010.03.001 – volume: 42 start-page: 149 issue: 1 year: 2019 ident: pone.0245026.ref011 article-title: Developing of robust and high accurate ECG beat classification by combining Gaussian mixtures and wavelets features publication-title: Australasian Physical and Engineering Sciences in Medicine doi: 10.1007/s13246-019-00722-z – volume: 110 start-page: 1097 issue: 8 year: 2012 ident: pone.0245026.ref002 article-title: The intersection between aging and cardiovascular disease publication-title: Circulation Research doi: 10.1161/CIRCRESAHA.111.246876 – volume: 2018 year: 2018 ident: pone.0245026.ref016 article-title: Secondary prevention of cardiovascular diseases and application of technology for early diagnosis publication-title: BioMed Research International doi: 10.1155/2018/5767864 – volume: 109 start-page: 253 issue: 3 year: 2017 ident: pone.0245026.ref028 article-title: Vascular ageing and arterial stiffness publication-title: Arquivos Brasileiros de Cardiologia – start-page: 1 year: 2015 ident: pone.0245026.ref025 article-title: A critical review of recurrent neural networks for sequence learning publication-title: arXiv – volume: 42 start-page: 76 issue: 1 year: 2013 ident: pone.0245026.ref020 article-title: Cohort profile: TwinsUK and healthy ageing twin study publication-title: International Journal of Epidemiology doi: 10.1093/ije/dyr207 – volume: 39 issue: 5 year: 2018 ident: pone.0245026.ref021 article-title: Assessing mental stress from the photoplethysmogram: A numerical study publication-title: Physiological Measurement doi: 10.1088/1361-6579/aabe6a – volume: 30 start-page: 445 issue: 3 year: 2012 ident: pone.0245026.ref009 article-title: Expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity publication-title: Journal of Hypertension doi: 10.1097/HJH.0b013e32834fa8b0 – volume: 27 start-page: 88 issue: 2 year: 2018 ident: pone.0245026.ref033 article-title: The usefulness of a single arm cuff oscillometric method (Arteriograph) to assess changes in central aortic blood pressure and arterial stiffness by antihypertensive treatment: results from the Doxazosin-Ramipril Study publication-title: Blood Pressure doi: 10.1080/08037051.2017.1394791 – volume: 3 start-page: 79 issue: 2 year: 2009 ident: pone.0245026.ref035 article-title: Limitations and pitfalls of non-invasive measurement of arterial pressure wave reflections and pulse wave velocity publication-title: Artery Research doi: 10.1016/j.artres.2009.02.006 – volume: 6 start-page: 428 issue: 2 year: 2019 ident: pone.0245026.ref014 article-title: Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics publication-title: ESC Heart Failure doi: 10.1002/ehf2.12419 – volume: 24 start-page: 69 issue: September year: 2012 ident: pone.0245026.ref039 article-title: Statistics Corner: A guide to appropriate use of correlation coefficient in medical resaerch publication-title: Malawi Medical Journal |
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| Title | Estimating pulse wave velocity from the radial pressure wave using machine learning algorithms |
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