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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: 1] [Impact Index Per Article: 0.5] [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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Xu C, Sellke FW, Abid MR. Assessments of microvascular function in organ systems. Am J Physiol Heart Circ Physiol 2022; 322:H891-H905. [PMID: 35333121 PMCID: PMC9037705 DOI: 10.1152/ajpheart.00589.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/22/2022] [Accepted: 03/22/2022] [Indexed: 01/23/2023]
Abstract
Microvascular disease plays critical roles in the dysfunction of all organ systems, and there are many methods available to assess the microvasculature. These methods can either assess the target organ directly or assess an easily accessible organ such as the skin or retina so that inferences can be extrapolated to the other systems and/or related diseases. Despite the abundance of exploratory research on some of these modalities and their possible applications, there is a general lack of clinical use. This deficiency is likely due to two main reasons: the need for standardization of protocols to establish a role in clinical practice or the lack of therapies targeted toward microvascular dysfunction. Also, there remain some questions to be answered about the coronary microvasculature, as it is complex, heterogeneous, and difficult to visualize in vivo even with advanced imaging technology. This review will discuss novel approaches that are being used to assess microvasculature health in several key organ systems, and evaluate their clinical utility and scope for further development.
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Affiliation(s)
- Cynthia Xu
- Cardiovascular Research Center, Rhode Island Hospital, Providence, Rhode Island
- Division of Cardiothoracic Surgery, Alpert Medical School of Brown University and Rhode Island Hospital, Providence, Rhode Island
| | - Frank W Sellke
- Cardiovascular Research Center, Rhode Island Hospital, Providence, Rhode Island
- Division of Cardiothoracic Surgery, Alpert Medical School of Brown University and Rhode Island Hospital, Providence, Rhode Island
| | - M Ruhul Abid
- Cardiovascular Research Center, Rhode Island Hospital, Providence, Rhode Island
- Division of Cardiothoracic Surgery, Alpert Medical School of Brown University and Rhode Island Hospital, Providence, Rhode Island
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Improving Cuff-Less Continuous Blood Pressure Estimation with Linear Regression Analysis. ELECTRONICS 2022. [DOI: 10.3390/electronics11091442] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
In this work, the authors investigate the cuff-less estimation of continuous BP through pulse transit time (PTT) and heart rate (HR) using regression techniques, which is intended as a first step towards continuous BP estimation with a low error, according to AAMI guidelines. Hypertension (the ‘silent killer’) is one of the main risk factors for cardiovascular diseases (CVDs), which are the main cause of death worldwide. Its continuous monitoring can offer a valid tool for patient care, as blood pressure (BP) is a significant indicator of health and, using it together with other parameters, such as heart and breath rates, could strongly improve prevention of CVDs. The novelties introduced in this work are represented by the implementation of pre-processing and by the innovative method for features research and features processing to continuously monitor blood pressure in a non-invasive way. Currently, invasive methods are the only reliable methods for continuous monitoring, while non-invasive techniques measure the values every few minutes. The proposed approach can be considered the first step for the integration of these types of algorithms on wearable devices, in particular on those developed for the SINTEC project.
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Vavrinsky E, Esfahani NE, Hausner M, Kuzma A, Rezo V, Donoval M, Kosnacova H. The Current State of Optical Sensors in Medical Wearables. BIOSENSORS 2022; 12:217. [PMID: 35448277 PMCID: PMC9029995 DOI: 10.3390/bios12040217] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 05/04/2023]
Abstract
Optical sensors play an increasingly important role in the development of medical diagnostic devices. They can be very widely used to measure the physiology of the human body. Optical methods include PPG, radiation, biochemical, and optical fiber sensors. Optical sensors offer excellent metrological properties, immunity to electromagnetic interference, electrical safety, simple miniaturization, the ability to capture volumes of nanometers, and non-invasive examination. In addition, they are cheap and resistant to water and corrosion. The use of optical sensors can bring better methods of continuous diagnostics in the comfort of the home and the development of telemedicine in the 21st century. This article offers a large overview of optical wearable methods and their modern use with an insight into the future years of technology in this field.
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Affiliation(s)
- Erik Vavrinsky
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
- Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Faculty of Medicine, Comenius University, Sasinkova 2, 81272 Bratislava, Slovakia
| | - Niloofar Ebrahimzadeh Esfahani
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
| | - Michal Hausner
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
| | - Anton Kuzma
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
| | - Vratislav Rezo
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
| | - Martin Donoval
- Institute of Electronics and Photonics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219 Bratislava, Slovakia; (N.E.E.); (M.H.); (A.K.); (V.R.); (M.D.)
| | - Helena Kosnacova
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University, Sasinkova 4, 81272 Bratislava, Slovakia
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy Sciences, Dubravska Cesta 9, 84505 Bratislava, Slovakia
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Loh HW, Xu S, Faust O, Ooi CP, Barua PD, Chakraborty S, Tan RS, Molinari F, Acharya UR. Application of photoplethysmography signals for healthcare systems: An in-depth review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106677. [PMID: 35139459 DOI: 10.1016/j.cmpb.2022.106677] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shuting Xu
- Cogninet Australia, Sydney, New South Wales 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 169609, Singapore; Duke-NUS Medical School, 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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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: 49] [Impact Index Per Article: 24.5] [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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Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning. Int J Hypertens 2021; 2021:9938584. [PMID: 34394983 PMCID: PMC8360747 DOI: 10.1155/2021/9938584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/28/2021] [Indexed: 01/11/2023] Open
Abstract
Background Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction. Methods The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods. Results Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment-300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN-GoogLeNet. Conclusions We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.
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He Q, Sun Z, Li Y, Wang W, Wang RK. Spatiotemporal monitoring of changes in oxy/deoxy-hemoglobin concentration and blood pulsation on human skin using smartphone-enabled remote multispectral photoplethysmography. BIOMEDICAL OPTICS EXPRESS 2021; 12:2919-2937. [PMID: 34168907 PMCID: PMC8194624 DOI: 10.1364/boe.423160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/19/2021] [Accepted: 04/19/2021] [Indexed: 06/13/2023]
Abstract
We propose a smartphone-enabled remote multispectral photoplethysmography (SP-rmPPG) system and method to realize spatiotemporal monitoring of perfusion changes and pulsations of the oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) information of the effective blood volume within light interrogated skin tissue beds. The system is implemented on an unmodified smartphone utilizing its built-in camera and flashlight to acquire videos of the skin reflectance. The SP-rmPPG method converts the RGB video into multispectral cubes, upon which to decouple the dynamic changes in HbO2 and Hb information using a modified Beer-Lambert law and the selective wavelength bands of 500 nm and 650 nm. Blood pulsation amplitudes are then obtained by applying a window-based lock-in amplification on the derived spatiotemporal changes in HbO2 or Hb signals. To demonstrate the feasibility of proposed method, we conduct two experiments on the skin tissue beds that are conditioned by occlusive maneuver of supplying arteries: one using the popular blood cuff pressure maneuver on the upper arm, and another artificially inducing a transient ischemic condition on the facial skin tissue beds by finger pressing on the supplying external carotid artery. The cuff experiment shows that the measured dynamic information of HbO2 and Hb in the downstream agrees well with the parallel measurements of oxygenation saturation given by the standard pulse oximeter. We also observe the expected imbalance of spatiotemporal changes in the HbO2 and Hb between the right and left cheeks when the transient ischemic condition is induced in the one side of facial skin tissue beds. The results from the two experiments sufficiently demonstrate the feasibility of the proposed method to monitor the spatiotemporal changes in the skin hemodynamics, including blood oxygenation and pulsation amplitudes. Considering the ever-growing accessibility and affordability of the smartphone to the general public, the proposed strategy promises the early screening of vascular diseases and improving general public health particularly in rural areas with low resource settings.
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Affiliation(s)
- Qinghua He
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Zhiyuan Sun
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Yuandong Li
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Wendy Wang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Ruikang K. Wang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
- Department of Ophthalmology, University of Washington, Seattle, WA98105, USA
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Shumate T, Link M, Furness J, Kemp-Smith K, Simas V, Climstein M. Validity of the Polar Vantage M watch when measuring heart rate at different exercise intensities. PeerJ 2021; 9:e10893. [PMID: 33614295 PMCID: PMC7879937 DOI: 10.7717/peerj.10893] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 01/12/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND The use of wrist worn wearable fitness trackers has been growing rapidly over the last decade. The growing popularity can be partly attributed to the improvements in technology, making activity trackers more affordable, comfortable and convenient for use in different fitness and environmental applications. Fitness trackers typically monitor activity level, track steps, distance, heart rate (HR), sleep, peripheral capillary oxygen saturation and more, as the technology continuously is advancing. In terms of measuring HR, photoplethysmography (PPG) is a relatively new technology utilised in wearables. PPG estimates HR through an optical technique that monitors changes in blood volume beneath the skin. With these new products becoming available it is important that the validity of these devices be evaluated. Therefore, the aim of this study was to assess the validity of the Polar Vantage M (PVM) watch to measure HR compared to medical grade ECG on a healthy population during a range of treadmill exercise intensities. METHODS A total of 30 healthy participants (n = 17 males, n = 13 females) were recruited for this study. The validity of the PVM watch to measure HR was compared against the gold standard 5-lead ECG. The study was conducted on 2 separate testing days with 24-48 h between sessions. Participants completed the Bruce Treadmill Protocol, and HR was measured every 30 s. Validation of the PVM watch in comparison to the ECG was measured with an Intraclass Correlation Coefficient (ICC) and associated 95% confidence intervals (CI) and levels of agreement were identified with Bland-Altman plots with 90% limits of agreement. Linear regression analysis was performed to calculate the value of r 2 computing the variation of HR obtained by the PVM watch and ECG. RESULTS In total, 30 participants completed the protocol, with data from 28 participants utilised for statistical analysis (16 males, 14 females, 26.10 ± 3.39 years, height 52.36 m ± 7.40 cm, mass 73.59 ± 11.90 kg). A strong and significant correlation was found between the PVM watch and ECG, demonstrating good criterion validity (p < 0.05, r 2 = 0.87). Good validity was seen for day 1 and day 2 for stage 0 (ICC = 0.83; 95% CI [0.63-0.92], ICC = 0.74; 95% CI [0.37-0.88]), stage 1 (ICC = 0.78; 95% CI [0.52-0.90], ICC = 0.88; 95% CI [0.74-0.95]), and stage 2 (ICC = 0.88; 95% CI [0.73-0.94], ICC = 0.80; 95% CI [0.40-0.92]). Poor validity was demonstrated on day 1 and day 2 for stages 3-5 (ICC < 0.50). CONCLUSION This study revealed that the PVM watch had a strong correlation with the ECG throughout the entire Bruce Protocol, however the level of agreement (LoA) becomes widely dispersed as exercise intensities increased. Due to the large LoA between the ECG and PVM watch, it is not advisable to use this device in clinical populations in which accurate HR measures are essential for patient safety; however, the watch maybe used in settings where less accurate HR is not critical to an individual's safety while exercising.
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Affiliation(s)
- Tricia Shumate
- Department of Physiotherapy, Faculty of Health Science and Medicine, Bond University, Gold Coast, QLD, Australia
| | - Magdalen Link
- Department of Physiotherapy, Faculty of Health Science and Medicine, Bond University, Gold Coast, QLD, Australia
| | - James Furness
- Department of Physiotherapy, Faculty of Health Science and Medicine, Bond University, Gold Coast, QLD, Australia
- Water Based Research Unit, Bond University, Gold Coast, QLD, Australia
| | - Kevin Kemp-Smith
- Department of Physiotherapy, Faculty of Health Science and Medicine, Bond University, Gold Coast, QLD, Australia
- Water Based Research Unit, Bond University, Gold Coast, QLD, Australia
| | - Vini Simas
- Department of Physiotherapy, Faculty of Health Science and Medicine, Bond University, Gold Coast, QLD, Australia
- Water Based Research Unit, Bond University, Gold Coast, QLD, Australia
| | - Mike Climstein
- Water Based Research Unit, Bond University, Gold Coast, QLD, Australia
- Clinical Exercise Physiology, School of Health and Human Sciences, Southern Cross University, Bilinga, QLD, Australia
- Physical Activity, Lifestyle, Ageing and Wellbeing, Faculty Research Group, Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia
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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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Chowdhury MH, Shuzan MNI, Chowdhury ME, Mahbub ZB, Uddin MM, Khandakar A, Reaz MBI. Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques. SENSORS 2020; 20:s20113127. [PMID: 32492902 PMCID: PMC7309072 DOI: 10.3390/s20113127] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 02/07/2023]
Abstract
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.
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Affiliation(s)
- Moajjem Hossain Chowdhury
- Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh; (M.H.C.); (M.N.I.S.)
| | - Md Nazmul Islam Shuzan
- Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh; (M.H.C.); (M.N.I.S.)
| | - Muhammad E.H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar;
- Correspondence: ; Tel.: +974-31010775
| | - Zaid B. Mahbub
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh; (Z.B.M.); (M.M.U.)
| | - M. Monir Uddin
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh; (Z.B.M.); (M.M.U.)
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar;
- Department of Mathematics and Physics, North South University, Dhaka 1229, Bangladesh; (Z.B.M.); (M.M.U.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic & Systems Engineering, Universiti Kebangsaan Malaysia, Bangi Selangor 43600, Malaysia;
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Elgendi M, Fletcher R, Liang Y, Howard N, Lovell NH, Abbott D, Lim K, Ward R. The use of photoplethysmography for assessing hypertension. NPJ Digit Med 2019; 2:60. [PMID: 31388564 PMCID: PMC6594942 DOI: 10.1038/s41746-019-0136-7] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 06/05/2019] [Indexed: 12/13/2022] Open
Abstract
The measurement of blood pressure (BP) is critical to the treatment and management of many medical conditions. High blood pressure is associated with many chronic disease conditions, and is a major source of mortality and morbidity around the world. For outpatient care as well as general health monitoring, there is great interest in being able to accurately and frequently measure BP outside of a clinical setting, using mobile or wearable devices. One possible solution is photoplethysmography (PPG), which is most commonly used in pulse oximetry in clinical settings for measuring oxygen saturation. PPG technology is becoming more readily available, inexpensive, convenient, and easily integrated into portable devices. Recent advances include the development of smartphones and wearable devices that collect pulse oximeter signals. In this article, we review (i) the state-of-the-art and the literature related to PPG signals collected by pulse oximeters, (ii) various theoretical approaches that have been adopted in PPG BP measurement studies, and (iii) the potential of PPG measurement devices as a wearable application. Past studies on changes in PPG signals and BP are highlighted, and the correlation between PPG signals and BP are discussed. We also review the combined use of features extracted from PPG and other physiological signals in estimating BP. Although the technology is not yet mature, it is anticipated that in the near future, accurate, continuous BP measurements may be available from mobile and wearable devices given their vast potential.
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Affiliation(s)
- Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Richard Fletcher
- D-Lab, Massachusetts Institute of Technology, Cambridge, MA USA
- Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA USA
| | - Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Howard Brain Sciences Foundation, Providence, Rhode Island USA
| | - Nigel H. Lovell
- Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW Australia
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA Australia
- Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA Australia
| | - Kenneth Lim
- Department of Obstetrics & Gynecology, University of British Columbia, Vancouver, Canada
- BC Children’s & Women’s Hospital, Vancouver, Canada
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
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Sagaidachnyi A, Fomin A, Usanov D, Skripal A. Real-time technique for conversion of skin temperature into skin blood flow: human skin as a low-pass filter for thermal waves. Comput Methods Biomech Biomed Engin 2019; 22:1009-1019. [DOI: 10.1080/10255842.2019.1615058] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Andrey Sagaidachnyi
- Department of Nano – and Biomedical Technology, Saratov State University, Saratov, Russia
| | - Andrey Fomin
- Department of Nano – and Biomedical Technology, Saratov State University, Saratov, Russia
| | - Dmitry Usanov
- Department of Nano – and Biomedical Technology, Saratov State University, Saratov, Russia
| | - Anatoly Skripal
- Department of Nano – and Biomedical Technology, Saratov State University, Saratov, Russia
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14
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Choi BM, Park C, Lee YH, Shin H, Lee SH, Jeong S, Noh GJ, Lee B. Development of a new analgesic index using nasal photoplethysmography. Anaesthesia 2018; 73:1123-1130. [PMID: 29790159 DOI: 10.1111/anae.14327] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/16/2018] [Indexed: 11/29/2022]
Abstract
Although surrogate measures to quantify pain intensity have been commercialised, there is a need to develop a new index with improved accuracy. The aim of this study was to develop a new analgesic index using nasal photoplethysmography data. The specially designed sensor was placed between the columella and the nasal septum to acquire nasal photoplethysmography in surgical patients. Nasal photoplethysmography and Surgical Pleth Index® (GE Healthcare) data were obtained for 14 min both in the absence (pre-operatively) or presence (postoperatively) of pain in a group of surgical patients, each patient acting as their own control. Various dynamic photoplethysmography variables were extracted to quantify pain intensity; the most accurate index was selected using logistic regression as a classifier. The area under the curve of the receiver-operating characteristic curve was measured to evaluate the accuracy of final model predictions. In total, 12,012 heart beats from 89 patients were used to develop a new Nasal Photoplethysmography Index for analgesic depth quantification. The two-variable model (a combination of diastolic peak point variation and heart beat interval variation) was most accurate in discriminating between the presence and absence of pain (numerical rating scale (NRS) ≥ 3). The accuracy and area under the curve of the receiver-operating characteristic curve for the Nasal Photoplethysmography Index were 75.3% and 0.8018, respectively, and 64.8% and 0.7034, respectively, for the Surgical Pleth Index. The Nasal Photoplethysmography Index clearly distinguished pain (NRS ≥ 3) in awake surgical patients with postoperative pain. The Nasal Photoplethysmography Index performed better than the Surgical Pleth Index. Further validation studies are needed to evaluate its feasibility to quantify pain intensity during general anaesthesia.
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Affiliation(s)
- B M Choi
- Department of Anaesthesiology and Pain Medicine, Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - C Park
- School of Mechanical Engineering, Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Y H Lee
- Department of Anaesthesiology and Pain Medicine, Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - H Shin
- Department of Biomedical Engineering, Chonnam National University, Yeosu, South Korea
| | - S H Lee
- New Drug Development Center, Osong Medical Innovation Foundation, Chungcheongbuk-do, Korea
| | - S Jeong
- Department of Anesthesiology and Pain Medicine, Chonnam National University Medical School, Gwangju, Korea
| | - G J Noh
- Department of Anesthesiology and Pain Medicine and Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - B Lee
- Department of Biomedical Science and Engineering, Institute of Integrated Technology, Gwangju Institute of Science and Technology, Gwangju, South Korea
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15
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Liang Y, Chen Z, Liu G, Elgendi M. A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China. Sci Data 2018; 5:180020. [PMID: 29485624 PMCID: PMC5827692 DOI: 10.1038/sdata.2018.20] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 12/19/2017] [Indexed: 12/29/2022] Open
Abstract
Open clinical trial data provide a valuable opportunity for researchers worldwide to assess new hypotheses, validate published results, and collaborate for scientific advances in medical research. Here, we present a health dataset for the non-invasive detection of cardiovascular disease (CVD), containing 657 data segments from 219 subjects. The dataset covers an age range of 20-89 years and records of diseases including hypertension and diabetes. Data acquisition was carried out under the control of standard experimental conditions and specifications. This dataset can be used to carry out the study of photoplethysmograph (PPG) signal quality evaluation and to explore the intrinsic relationship between the PPG waveform and cardiovascular disease to discover and evaluate latent characteristic information contained in PPG signals. These data can also be used to study early and noninvasive screening of common CVD such as hypertension and other related CVD diseases such as diabetes.
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Affiliation(s)
- Yongbo Liang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China.,School of Electrical and Computer Engineering, University of British Columbia, Columbia, Vancouver V6T 1Z4, Canada
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, PR China
| | - Guiyong Liu
- Guilin People's Hospital, Guilin 541000, PR China
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Columbia, Vancouver V6T 1Z4, Canada.,Department of Obstetrics & Gynecology, University of British Columbia, Columbia, Vancouver V6H 3N1, Canada.,BC Children's & Women's Hospital, Vancouver, Vancouver V6H 3N1, Canada
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Siddiqui SA, Zhang Y, Feng Z, Kos A. A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera. J Med Syst 2016; 40:126. [PMID: 27067432 DOI: 10.1007/s10916-016-0485-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 03/28/2016] [Indexed: 11/25/2022]
Abstract
The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively.
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Affiliation(s)
- Sarah Ali Siddiqui
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Yuan Zhang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China.
| | - Zhiquan Feng
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Anton Kos
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
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