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Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
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Iqbal S, Bacardit J, Griffiths B, Allen J. Deep learning classification of systemic sclerosis from multi-site photoplethysmography signals. Front Physiol 2023; 14:1242807. [PMID: 37781233 PMCID: PMC10534001 DOI: 10.3389/fphys.2023.1242807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 08/18/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction: A pilot study assessing a novel approach to identify patients with Systemic Sclerosis (SSc) using deep learning analysis of multi-site photoplethysmography (PPG) waveforms ("DL-PPG"). Methods: PPG recordings having baseline, unilateral arm pressure cuff occlusion and reactive hyperaemia flush phases from 6 body sites were studied in 51 Controls and 20 SSc patients. RGB scalogram images were obtained from the PPG, using the continuous wavelet transform (CWT). 2 different pre-trained convolutional neural networks (CNNs, namely, GoogLeNet and EfficientNetB0) were trained to classify the SSc and Control groups, evaluating their performance using 10-fold stratified cross validation (CV). Their classification performance (i.e., accuracy, sensitivity, and specificity, with 95% confidence intervals) was also compared to traditional machine learning (ML), i.e., Linear Discriminant Analysis (LDA) and K-Nearest Neighbour (KNN). Results: On a participant basis DL-PPG accuracy, sensitivity and specificity for GoogLeNet were 83.1 (72.3-90.9), 75.0 (50.9-91.3) and 86.3 (73.7-94.3)% respectively, and for EfficientNetB0 were 87.3 (77.2-94.0), 80.0 (56.3-94.3) and 90.1 (78.6-96.7)%. The corresponding results for ML classification using LDA were 66.2 (53.9-77.0), 65.0 (40.8-84.6) and 66.7 (52.1-79.2)% respectively, and for KNN were 76.1 (64.5-85.4), 40.0 (19.1-63.9), and 90.2 (78.6-96.7)% respectively. Discussion: This study shows the potential of DL-PPG classification using CNNs to detect SSc. EfficientNetB0 gave an overall improved performance compared to GoogLeNet, with both CNNs performing better than the traditional ML methods tested. Our automatic AI approach, using transfer learning, could offer significant benefits for SSc diagnostics in a variety of clinical settings where low-cost portable and easy-to-use diagnostics can be beneficial.
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Affiliation(s)
- Sadaf Iqbal
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle Upon Tyne, United Kingdom
| | - Jaume Bacardit
- School of Computing, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Bridget Griffiths
- Department of Rheumatology, Freeman Hospital, Newcastle Upon Tyne, United Kingdom
| | - John Allen
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- Northern Medical Physics and Clinical Engineering, Freeman Hospital, Newcastle Upon Tyne, United Kingdom
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
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Baciu VE, Lambert Cause J, Solé Morillo Á, García-Naranjo JC, Stiens J, da Silva B. Anomaly Detection in Multi-Wavelength Photoplethysmography Using Lightweight Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2023; 23:6947. [PMID: 37571730 PMCID: PMC10422657 DOI: 10.3390/s23156947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/18/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Over the past few years, there has been increased interest in photoplethysmography (PPG) technology, which has revealed that, in addition to heart rate and oxygen saturation, the pulse shape of the PPG signal contains much more valuable information. Lately, the wearable market has shifted towards a multi-wavelength and multichannel approach to increase signal robustness and facilitate the extraction of other intrinsic information from the signal. This transition presents several challenges related to complexity, accuracy, and reliability of algorithms. To address these challenges, anomaly detection stages can be employed to increase the accuracy and reliability of estimated parameters. Powerful algorithms, such as lightweight machine learning (ML) algorithms, can be used for anomaly detection in multi-wavelength PPG (MW-PPG). The main contributions of this paper are (a) proposing a set of features with high information gain for anomaly detection in MW-PPG signals in the classification context, (b) assessing the impact of window size and evaluating various lightweight ML models to achieve highly accurate anomaly detection, and (c) examining the effectiveness of MW-PPG signals in detecting artifacts.
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Affiliation(s)
- Vlad-Eusebiu Baciu
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | - Joan Lambert Cause
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
- Department of Biomedical Engineering, Universidad de Oriente, Santiago de Cuba 90500, Cuba
| | - Ángel Solé Morillo
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | | | - Johan Stiens
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
| | - Bruno da Silva
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium; (J.L.C.); (Á.S.M.); (J.S.)
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Andreozzi E, Sabbadini R, Centracchio J, Bifulco P, Irace A, Breglio G, Riccio M. Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197566. [PMID: 36236663 PMCID: PMC9570799 DOI: 10.3390/s22197566] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/26/2022] [Accepted: 10/01/2022] [Indexed: 05/31/2023]
Abstract
Pulse waves (PWs) are mechanical waves that propagate from the ventricles through the whole vascular system as brisk enlargements of the blood vessels' lumens, caused by sudden increases in local blood pressure. Photoplethysmography (PPG) is one of the most widespread techniques employed for PW sensing due to its ability to measure blood oxygen saturation. Other sensors and techniques have been proposed to record PWs, and include applanation tonometers, piezoelectric sensors, force sensors of different kinds, and accelerometers. The performances of these sensors have been analyzed individually, and their results have been found not to be in good agreement (e.g., in terms of PW morphology and the physiological parameters extracted). Such a comparison has led to a deeper comprehension of their strengths and weaknesses, and ultimately, to the consideration that a multimodal approach accomplished via sensor fusion would lead to a more robust, reliable, and potentially more informative methodology for PW monitoring. However, apart from various multichannel and multi-site systems proposed in the literature, no true multimodal sensors for PW recording have been proposed yet that acquire PW signals simultaneously from the same measurement site. In this study, a true multimodal PW sensor is presented, which was obtained by integrating a piezoelectric forcecardiography (FCG) sensor and a PPG sensor, thus enabling simultaneous mechanical-optical measurements of PWs from the same site on the body. The novel sensor performance was assessed by measuring the finger PWs of five healthy subjects at rest. The preliminary results of this study showed, for the first time, that a delay exists between the PWs recorded simultaneously by the PPG and FCG sensors. Despite such a delay, the pulse waveforms acquired by the PPG and FCG sensors, along with their first and second derivatives, had very high normalized cross-correlation indices in excess of 0.98. Six well-established morphological parameters of the PWs were compared via linear regression, correlation, and Bland-Altman analyses, which showed that some of these parameters were not in good agreement for all subjects. The preliminary results of this proof-of-concept study must be confirmed in a much larger cohort of subjects. Further investigation is also necessary to shed light on the physical origin of the observed delay between optical and mechanical PW signals. This research paves the way for the development of true multimodal, wearable, integrated sensors and for potential sensor fusion approaches to improve the performance of PW monitoring at various body sites.
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Charlton PH, Paliakaitė B, Pilt K, Bachler M, Zanelli S, Kulin D, Allen J, Hallab M, Bianchini E, Mayer CC, Terentes-Printzios D, Dittrich V, Hametner B, Veerasingam D, Žikić D, Marozas V. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol 2022; 322:H493-H522. [PMID: 34951543 PMCID: PMC8917928 DOI: 10.1152/ajpheart.00392.2021] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 12/07/2022]
Abstract
The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular aging, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarizes research into assessing vascular age from the PPG. Three categories of approaches are described: 1) those which use a single PPG signal (based on pulse wave analysis), 2) those which use multiple PPG signals (such as pulse transit time measurement), and 3) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realize the full potential of photoplethysmography for assessing vascular age.
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Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Research Centre for Biomedical Engineering, University of London, London, United Kingdom
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Kristjan Pilt
- Department of Health Technologies, Tallinn University of Technology, Tallinn, Estonia
| | - Martin Bachler
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Serena Zanelli
- Laboratoire Analyze, Géométrie et Applications, University Sorbonne Paris Nord, Paris, France
- Axelife, Redon, France
| | - Dániel Kulin
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
- E-Med4All Europe, Limited, Budapest, Hungary
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Magid Hallab
- Axelife, Redon, France
- Centre de recherche et d'Innovation, Clinique Bizet, Paris, France
| | | | - Christopher C Mayer
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dimitrios Terentes-Printzios
- Hypertension and Cardiometabolic Unit, First Department of Cardiology, Hippokration Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
| | - Verena Dittrich
- Redwave Medical, Gesellschaft mit beschränkter Haftung, Jena, Germany
| | - Bernhard Hametner
- Biomedical Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology, Seibersdorf, Austria
| | - Dave Veerasingam
- Department of Cardiothoracic Surgery, Galway University Hospitals, Galway, Ireland
| | - Dejan Žikić
- Faculty of Medicine, Institute of Biophysics, University of Belgrade, Belgrade, Serbia
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
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Park J, Seok HS, Kim SS, Shin H. Photoplethysmogram Analysis and Applications: An Integrative Review. Front Physiol 2022; 12:808451. [PMID: 35300400 PMCID: PMC8920970 DOI: 10.3389/fphys.2021.808451] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/21/2021] [Indexed: 12/03/2022] Open
Abstract
Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
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Affiliation(s)
- Junyung Park
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hyeon Seok Seok
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Sang-Su Kim
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - Hangsik Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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Bilim S, Içağasioğlu A, Akbal A, Kasapoğlu E, Gürsel S. Assessment of subclinical atherosclerosis with ankle-brachial index in psoriatic arthritis: A case-control study. Arch Rheumatol 2021; 36:210-218. [PMID: 34527925 PMCID: PMC8418778 DOI: 10.46497/archrheumatol.2021.8083] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 08/11/2020] [Indexed: 11/03/2022] Open
Abstract
Objectives This study aims to evaluate subclinical atherosclerosis using the Ankle-Brachial Index (ABI) in patients with psoriatic arthritis (PsA). Patients and methods This case-control study included 51 PsA patients (24 males, 27 females; median age 47; range, 41 to 52 years) recruited at our hospital's outpatient clinics between October 2016 and January 2017 and 50 healthy controls (24 males, 26 females; median age: 48.5; range, 40.7 to 56 years). Anthropomorphic measurements and laboratory results were recorded. In patients, the 66 swollen/68 tender joints count, dactylitis score, Leeds Enthesitis Index, Health-related Quality of Life, the Psoriasis Area and Severity Index, and Dermatology Life Quality Index were evaluated. Ankylosing Spondylitis Quality of Life and Bath Ankylosing Spondylitis Disease Activity Index were applied to patients with axial disease. Then, Composite Psoriatic Disease Activity Index was determined. A Doppler probe and a standard blood pressure cuff were used to calculate the ABI values for each participant. Results Patients had lower right ABI (median, 1.05 vs. 1.1, p<0.01), lower left ABI (1.04 vs. 1.09, p<0.01) and lower overall ABI (1.03 vs. 1.09, p<0.01) compared with healthy subjects. Twelve (23.5%) patients had borderline ABI, but none of the controls (p<0.01). Patients with borderline ABI had a longer duration of psoriasis (25 vs. 15 years, p=0.03). The distribution of borderline ABI value was statistically significant between patients with axial disease and peripheral disease only (42.1% vs. 12.5%, p=0.02). Disease activity was found as an independent risk factor for borderline ABI in a binary logistic regression (odds ratio 6.306, 95% confidence interval 1.185 to 33.561, p=0.031). Conclusion Lower ABI was found in PsA patients than healthy controls even in those matched with traditional cardiovascular risk factors. All participants with borderline ABI were in the patient group. Borderline ABI was associated with disease activity and disease duration.
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Affiliation(s)
- Serhad Bilim
- Department of Physical Medicine and Rehabilitation, Division of Pain Medicine, Marmara University Faculty of Medicine, Istanbul, Turkey
| | - Afitap Içağasioğlu
- Department of Physical Medicine and Rehabilitation, Istanbul Medeniyet University Göztepe Training and Research Hospital, Istanbul, Turkey
| | - Ayla Akbal
- Department of Physiotherapy and Rehabilitation, Istanbul Bilim University, Istanbul, Turkey
| | - Esen Kasapoğlu
- Department of Internal Medicine, Division of Romatology, Istanbul Medeniyet University, Göztepe Training and Research Hospital, Istanbul, Turkey
| | - Sıdıka Gürsel
- Department of Cardiovascular Surgery, Istanbul Medeniyet University Göztepe Training and Research Hospital, Istanbul, Turkey
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Shin H, Park J, Seok HS, Kim SS. Photoplethysmogram analysis and applications: An Integrative Review (Preprint). JMIR BIOMEDICAL ENGINEERING 2020. [DOI: 10.2196/25567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Simonyan MA, Posnenkova OM, Kiselev AR. Capabilities of photoplethysmography as a method for screening of cardiovascular system pathology. CARDIO-IT 2020. [DOI: 10.15275/cardioit.2020.0102] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Currently, vegetative dysfunction considered to be one of principal mechanisms in the pathogenesis of cardiovascular pathology, which causes a cascade of events leading to changes in the properties and a structure of vascular wall. This review article contains literature from various databases (Russian science citation index, PubMed, Google Shcolar, Scopus). It presents the methods for assessing vegetative imbalance. In particular, the method of photoplethysmography (PPGV) is considered for recording periodic fluctuations at various frequencies in the distal vascular bed which characterize physiological processes (cardiac activity, respiratory influences, neurogenic, myogenic and endothelial activity). In addition, other diagnostic capabilities of PPGV such as heart rate (HR) assessment, determining the properties of vascular wall and the level of blood saturation are elucidated. This paper demonstrates a wide range of PPGV applications. The simplicity of PPGV reproduction and its cost-effectiveness make it feasible both in routine clinical practice for the purposes of screening for cardiovascular pathology, and for individual health monitoring incorporated in smart devices.
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Allen J, O'Sullivan J, Stansby G, Murray A. Age-related changes in pulse risetime measured by multi-site photoplethysmography. Physiol Meas 2020; 41:074001. [PMID: 32784270 DOI: 10.1088/1361-6579/ab9b67] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE It is accepted that changes in the peripheral pulse waveform characteristics occur with ageing. Pulse risetime is one important feature which has clinical value. However, it is unclear how it varies across the full age spectrum from child to senior and for different peripheral measurement sites. The objectives of this study were to determine the association between age and pulse risetime characteristics over an 8-decade age range at the ears, fingers, and toes, and to consider effects arising from differences in systolic blood pressure (SBP), height and heart rate. APPROACH Multi-site photoplethysmography (MPPG) pulse waveforms were recorded non-invasively from the right and left ears, fingers, and toes of 304 normal healthy human subjects (range 6-87 years; 156 male and 148 female). SBP, height, and heart rate were also measured. Multi-site PPG pulse risetimes, and their site differences, were determined. MAIN RESULTS Univariate regression analysis showed positive correlations with risetime for age (ears, fingers and toes: + 0.8, + 1.9, and + 1.1 ms/year, respectively), SBP (+0.5, + 1.3, and + 0.9 ms/mmHg) and height (+0.5, + 1.2, and + 1.0 ms/cm), but with a clear inverse association with heart rate (-1.8, - 2.5, and - 1.6 ms min) (P < 0.0001). No significant differences between male and female subjects were found for pulse risetime. SIGNIFICANCE Normative multi-site PPG risetime characteristics have been defined in over 300 subjects and are shown to increase with age linearly up to the 8th decade. In contrast, we have shown that heart rate has a clear inverse relationship with risetime for all measurement sites.
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Affiliation(s)
- John Allen
- Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, United Kingdom. Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
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Allen J. Quantifying the Delays Between Multi-Site Photoplethysmography Pulse and Electrocardiogram R-R Interval Changes Under Slow-Paced Breathing. Front Physiol 2019; 10:1190. [PMID: 31607946 PMCID: PMC6774289 DOI: 10.3389/fphys.2019.01190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 09/03/2019] [Indexed: 12/05/2022] Open
Abstract
Objective: Objective assessment of autonomic function is important, including the investigation of slow-paced breathing to induce associated periodic changes in the cardiovascular system – such as blood pressure and heart rate. However, pulse changes across a range of peripheral body sites have seldom been explored with this challenge. The primary aim of this pilot study was to utilize multi-site photoplethysmography (MPPG) technology to quantify the phase delays, i.e., correlation lags, between changes in heart rate and changes in key pulse features with slow-paced breathing (0.1 Hz). Methods: Waveforms were collected simultaneously from the right and left ear lobes, thumbs, and great toes of 18 healthy adult subjects. Cross correlation lags between reference beat-to-beat changes in electrocardiogram (ECG) R-R wave interval and changes in pulse arrival time (foot of pulse; PATf) and also for pulse amplitude (foot-to-peak; AMP) were determined. Results: Relative to R-R changes, the median ear, thumb, and toe PATf correlation lags were 3.4, 2.9, and 2.1 beats, respectively; contrasting to AMP with 5.7, 6.0, and 6.9 beats, respectively. These PATf correlation lags in beats were significantly lower than for the AMP measure. Segmental differences between sites and timing measure variability have also been quantified. Conclusion: This pilot study has indicated bilateral similarity plus segmental differences for relative delays in PPG pulse timing and amplitude measures relative to R-R interval changes with paced breathing. These correlation and variability data are now available for comparison with cardiovascular patient groups to support development of autonomic function assessment techniques.
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Affiliation(s)
- John Allen
- Microvascular Diagnostics, Northern Medical Physics and Clinical Engineering Department, Freeman Hospital, Newcastle upon Tyne, United Kingdom.,Faculty of Medical Sciences, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom
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Hartmann V, Liu H, Chen F, Qiu Q, Hughes S, Zheng D. Quantitative Comparison of Photoplethysmographic Waveform Characteristics: Effect of Measurement Site. Front Physiol 2019; 10:198. [PMID: 30890959 PMCID: PMC6412091 DOI: 10.3389/fphys.2019.00198] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 02/15/2019] [Indexed: 11/13/2022] Open
Abstract
Introduction: Photoplethysmography (PPG) has been widely used to assess cardiovascular function. However, few studies have comprehensively investigated the effect of measurement site on PPG waveform characteristics. This study aimed to provide a quantitative comparison on this. Methods: Thirty six healthy subjects participated in this study. For each subject, PPG signals were sequentially recorded for 1 min from six different body sites (finger, wrist under (anatomically volar), wrist upper (dorsal), arm, earlobe, and forehead) under both normal and deep breathing patterns. For each body site under a certain breathing pattern, the mean amplitude was firstly derived from recorded PPG waveform which was then normalized to derive several waveform characteristics including the pulse peak time (Tp), dicrotic notch time (Tn), and the reflection index (RI). The effects of breathing pattern and measurement site on the waveform characteristics were finally investigated by the analysis of variance (ANOVA) with post hoc multiple comparisons. Results: Under both breathing patterns, the PPG measurements from the finger achieved the highest percentage of analyzable waveforms for extracting waveform characteristics. There were significant effects of breathing pattern on Tn and RI (larger Tn and smaller RI with deep breathing on average, both p < 0.03). The effects of measurement site on mean amplitude, Tp, Tn, and RI were significant (all p < 0.001). The key results were that, under both breathing patterns, the mean amplitude from finger PPG was significantly larger and its Tp and RI were significantly smaller than those from the other five sites (all p < 0.001, except p = 0.04 for the Tp of "wrist under"), and Tn was only significantly larger than that from the earlobe (both p < 0.05). Conclusion: This study has quantitatively confirmed the effect of PPG measurement site on PPG waveform characteristics (including mean amplitude, Tp, Tn, and RI), providing scientific evidence for a better understanding of the PPG waveform variations between different body sites.
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Affiliation(s)
- Vera Hartmann
- Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Haipeng Liu
- Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom.,Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Qian Qiu
- Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Stephen Hughes
- Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Dingchang Zheng
- Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
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A Smart Pillow for Health Sensing System Based on Temperature and Humidity Sensors. SENSORS 2018; 18:s18113664. [PMID: 30380614 PMCID: PMC6263409 DOI: 10.3390/s18113664] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 10/18/2018] [Accepted: 10/26/2018] [Indexed: 12/21/2022]
Abstract
The quality of sleep affects the patient's health, along with the observation of vital life signs such as body temperature and sweat in sleep, is essential in the monitoring of sleep as well as clinical diagnosis. However, traditional methods in recording physiological change amidst sleep is difficult without being intrusive. The smart pillow is developed to provide a relatively easy way to observe one's sleep condition, employing temperature and humidity sensors by implanting them inside the pillow in strategic positions. With the patient's head on the pillow, the roles of sensors are identified as main, auxiliary or environmental temperature, based on the differences of value from three temperature sensors, thus the pattern of sleep can be extracted by statistical analysis, and the body temperature is inferred by a specially designed Fuzzy Logic System if the head-on position is stable for more than 15 min. Night sweat is reported on data from the humidity sensor. Therefore, a cloud-based health-sensing system is built in the smart pillow to collect and analyze data. Experiments from various individuals prove that statistical and inferred results reflect normal and abnormal conditions of sleep accurately. The daily sleeping information of patients from the pillow is helpful in the decision-making of diagnoses and treatment, and users can change their habits of sleep gradually by observing the data with their health professional.
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Sharkey EJ, Di Maria C, Klinge A, Murray A, Zheng D, O'Sullivan J, Allen J. Innovative multi-site photoplethysmography measurement and analysis demonstrating increased arterial stiffness in paediatric heart transplant recipients. Physiol Meas 2018; 39:074007. [PMID: 29791321 DOI: 10.1088/1361-6579/aac76a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE It has been documented that heart transplantation in children is often complicated by arterial hypertension and increased arterial stiffness. We use innovative multi-site photoplethysmography (MPPG) pulse measurement and analysis technology to assess changes in arterial stiffness in paediatric heart transplant recipients (HTRs) in comparison with healthy control (HC) children. APPROACH A group of 20 HTRs (median age 13.5 years, eight male) were compared to an overall age- and gender-matched group of 161 HCs (median age 11.6 years, 74 male). Peripheral pulse was recorded bilaterally using MPPG at the ear lobe, index finger and great toe sites, along with an electrocardiogram cardiac timing reference. Segmental pulse arrival times between peripheral sites (finger-ear, PATf-e; toe-finger, PATt-f; and toe-ear PATt-e) were calculated as arterial stiffness measures, and differences between subject groups were tested using multivariate analysis. Normalised ear, finger and toe pulse shapes were also studied and compared between groups. MAIN RESULTS After correction for heart rate and diastolic and mean arterial blood pressures, the HTR group was found to have significantly lower segmental PATt-e and PATt-f measurements, with median values of 150 ms versus 172 ms in the HC group (p = 0.02), and 104 ms versus 118 ms in the HC group (p = 0.01), respectively, consistent with increased arterial stiffness in the patient group. The normalised ear, finger and toe sites showed only a mild elongation in each pulse rise time for the transplant group. SIGNIFICANCE This study shows that innovative and easy-to-do MPPG gives further evidence for increased arterial stiffness in children who have undergone successful cardiac transplantation.
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Affiliation(s)
- Emma J Sharkey
- Microvascular Diagnostics, Northern Medical Physics and Clinical Engineering, Newcastle upon Tyne, NE7 7DN, United Kingdom. Department of Paediatric Cardiology, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom. Sharkey and Di Maria to be assigned as joint first authors
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BHAT SHREYA, ADAM MUHAMMAD, HAGIWARA YUKI, NG EDDIEY. THE BIOPHYSICAL PARAMETER MEASUREMENTS FROM PPG SIGNAL. J MECH MED BIOL 2017. [DOI: 10.1142/s021951941740005x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Early investigation on blood circulation by Hertzman (1937) leads to the observation of vital body signs such as respiration rate, heart rate (HR), blood oxygenation and vascular assessment using photoplethysmographic (PPG) device. PPG is a noninvasive, painless optical technique used to monitor the pulsations linked to alteration in the blood volume. The PPG waveform is a summation of pulsatile and nonpulsatile components and contains useful information about the physiological systems. With the breakthrough in technology and development of powerful analytical tools, PPG devices are constantly being used in advanced medical equipments such as smart-watches and smart-wristbands for HR monitoring, pulse oximeters for measuring respiratory rate and noncontact PPG device for blood oxygen saturation measurement. This paper presents description on PPG and its characteristic waveform and working principle. It also includes brief explanation on nonlinear analysis of PPG signals and salient applications of PPG followed by its advantages and limitations.
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Affiliation(s)
- SHREYA BHAT
- Department of Psychiatry, St John’s Research Institute, Bangalore, India
| | - MUHAMMAD ADAM
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - YUKI HAGIWARA
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - EDDIE Y. K. NG
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
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Watelet B, Jeancolas J, Lanéelle D, Bienvenu B, Le Hello C. [Prevalence of macrovascular arterial involvement of the 4 limbs in systemic sclerosis: About a case series of 14 patients]. Rev Med Interne 2017; 38:430-435. [PMID: 28602440 DOI: 10.1016/j.revmed.2016.12.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Revised: 12/21/2016] [Accepted: 12/22/2016] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Trophic disorders of the extremities are a common complication of systemic sclerosis (SSc), mainly related to microvascular damage. However, SSc seems to be a risk factor for premature athero-thrombotic disease that can affect the peripheral arteries, participate in the occurrence of trophic disorders and promote the occurrence of infectious complications. The objective of this study was to assess the prevalence of arterial disease of the limbs in SSc patients. METHODS Consecutive inclusions in the context of a multidisciplinary consultation centered on disability of the hand with collection of clinical data [cardiovascular risk factors (CVRF), history of trophic disorders of ischemic origin, peripheral pulse palpation, Allen maneuver the upper (UL) and lower limbs (LL)], and hemodynamic data (flow recorded by Doppler in radial, ulnar, anterior and posterior tibial arteries, and measurement of systolic indices ankles). RESULTS Fourteen patients were included (11 right-handers, 2 left-handers, 1 ambidextrous). The sex-ratio male/female was 0.27 and the average age of 58.1±10.4 years. The main CVRF were age and smoking. In the UL, 42.8% of patients had a history of trophic disorders, Allen maneuver was abnormal for 35.7% of the superficial palmar arch, 42.9% of ulnar pulse were not perceived and there was no recordable flow in 25% of ulnar artery. In the LL, 14.3% of patients had already presented trophic disorders toes, Allen maneuver was abnormal for 15.4% of the posterior tibial artery, 25.6% of posterior tibial pulse were not perceived and flow of 15.4% of posterior tibial arteries was pathological. CONCLUSION The distal macrovascular disease preferentially affecting the ulnar and posterior tibial arteries with a high frequency to the UL and two times less at LL. The pathophysiology is unclear but it could be a proper manifestation of SSc. It seems necessary that SSc patients have a strict balance of their CVRF and a screening of macrovascular arterial lesions. There is also the question of the place of an anti-atherosclerotic therapy in these patients.
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Affiliation(s)
- B Watelet
- Service de médecine vasculaire, CHU de Caen, avenue de la Côte-de-Nacre, 14033 Caen cedex, France.
| | - J Jeancolas
- Service de médecine vasculaire, CHU de Caen, avenue de la Côte-de-Nacre, 14033 Caen cedex, France
| | - D Lanéelle
- Service de médecine vasculaire, CHU de Caen, avenue de la Côte-de-Nacre, 14033 Caen cedex, France
| | - B Bienvenu
- Service de médecine interne, CHU de Caen, avenue de la Côte-de-Nacre, 14033 Caen cedex, France
| | - C Le Hello
- Service de médecine vasculaire, CHU de Caen, avenue de la Côte-de-Nacre, 14033 Caen cedex, France
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Meiszterics Z, Tímár O, Gaszner B, Faludi R, Kehl D, Czirják L, Szűcs G, Komócsi A. Early morphologic and functional changes of atherosclerosis in systemic sclerosis—a systematic review and meta-analysis. Rheumatology (Oxford) 2016; 55:2119-2130. [DOI: 10.1093/rheumatology/kew236] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Revised: 04/29/2016] [Indexed: 02/03/2023] Open
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