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Hametner B, Maurer S, Sehnert A, Bachler M, Orter S, Zechner O, Müllner-Rieder M, Penkler M, Wassertheurer S, Sehnert W, Mengden T, Mayer CC. Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention. Physiol Meas 2024; 45:055015. [PMID: 38688296 DOI: 10.1088/1361-6579/ad45ab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/30/2024] [Indexed: 05/02/2024]
Abstract
Background.Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective.The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Approach.A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.Mainresults.The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],p< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],p= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],p= 0.50) in the experimental group.Significance.PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.
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Affiliation(s)
- Bernhard Hametner
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
| | - Severin Maurer
- Institute of Market Research and Methodology, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria
| | - Alina Sehnert
- Institute for Clinical Research Sehnert, Dortmund, Germany
| | - Martin Bachler
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
| | - Stefan Orter
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
| | - Olivia Zechner
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
| | - Markus Müllner-Rieder
- AIT Austrian Institute of Technology, Center for Health & Bioresources, Digital Health Information Systems, Vienna, Austria
| | - Michael Penkler
- Institute of Market Research and Methodology, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria
| | - Siegfried Wassertheurer
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
| | - Walter Sehnert
- Institute for Clinical Research Sehnert, Dortmund, Germany
| | - Thomas Mengden
- Kerckhoff Clinic, Rehabilitation, ESH Excellence Centre, Bad Nauheim, Germany
| | - Christopher C Mayer
- AIT Austrian Institute of Technology, Center for Technology Experience, Experience Business Transformation, Vienna, Austria
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Shokouhmand A, Jiang X, Ayazi F, Ebadi N. MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure. IEEE J Biomed Health Inform 2024; 28:2699-2712. [PMID: 38442050 DOI: 10.1109/jbhi.2024.3372968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
OBJECTIVE To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings. METHODS A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV). RESULTS Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively. CONCLUSION The findings suggest high potential for SPG-based BP estimation at the fingertip. SIGNIFICANCE This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.
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Kaisti M, Panula T, Sirkiä JP, Pänkäälä M, Koivisto T, Niiranen T, Kantola I. Hemodynamic Bedside Monitoring Instrument with Pressure and Optical Sensors: Validation and Modality Comparison. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2307718. [PMID: 38647263 DOI: 10.1002/advs.202307718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 01/19/2024] [Indexed: 04/25/2024]
Abstract
Results from two independent clinical validation studies for measuring hemodynamics at the patient's bedside using a compact finger probe are reported. Technology comprises a barometric pressure sensor, and in one implementation, additionally, an optical sensor for photoplethysmography (PPG) is developed, which can be used to measure blood pressure and analyze rhythm, including the continuous detection of atrial fibrillation. The capabilities of the technology are shown in several form factors, including a miniaturized version resembling a common pulse oximeter to which the technology could be integrated in. Several main results are presented: i) the miniature finger probe meets the accuracy requirements of non-invasive blood pressure instrument validation standard, ii) atrial fibrillation can be detected during the blood pressure measurement and in a continuous recording, iii) a unique comparison between optical and pressure sensing mechanisms is provided, which shows that the origin of both modalities can be explained using a pressure-volume model and that recordings are close to identical between the sensors. The benefits and limitations of both modalities in hemodynamic monitoring are further discussed.
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Affiliation(s)
- Matti Kaisti
- Department of Computing, University of Turku, Faculty of Technology, Vesilinnantie 5, Turku, 20500, Finland
| | - Tuukka Panula
- Department of Computing, University of Turku, Faculty of Technology, Vesilinnantie 5, Turku, 20500, Finland
| | - Jukka-Pekka Sirkiä
- Department of Computing, University of Turku, Faculty of Technology, Vesilinnantie 5, Turku, 20500, Finland
| | - Mikko Pänkäälä
- Department of Computing, University of Turku, Faculty of Technology, Vesilinnantie 5, Turku, 20500, Finland
| | - Tero Koivisto
- Department of Computing, University of Turku, Faculty of Technology, Vesilinnantie 5, Turku, 20500, Finland
| | - Teemu Niiranen
- Department of Internal Medicine, University of Turku, Kiinamyllynkatu 4-8, Turku, 20521, Finland
| | - Ilkka Kantola
- Division of Medicine, Turku University Hospital, Kiinamyllynkatu 4-8, Turku, 20521, Finland
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Mathieu AJW, Pascual MS, Charlton PH, Volovaya M, Venton J, Aston PJ, Nandi M, Alastruey J. Advanced waveform analysis of the photoplethysmogram signal using complementary signal processing techniques for the extraction of biomarkers of cardiovascular function. JRSM Cardiovasc Dis 2024; 13:20480040231225384. [PMID: 38314325 PMCID: PMC10838030 DOI: 10.1177/20480040231225384] [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: 06/27/2023] [Revised: 12/08/2023] [Accepted: 12/09/2023] [Indexed: 02/06/2024] Open
Abstract
Introduction Photoplethysmogram signals from wearable devices typically measure heart rate and blood oxygen saturation, but contain a wealth of additional information about the cardiovascular system. In this study, we compared two signal-processing techniques: fiducial point analysis and Symmetric Projection Attractor Reconstruction, on their ability to extract new cardiovascular information from a photoplethysmogram signal. The aim was to identify fiducial point analysis and Symmetric Projection Attractor Reconstruction indices that could classify photoplethysmogram signals, according to age, sex and physical activity. Methods Three datasets were used: an in-silico dataset of simulated photoplethysmogram waves for healthy male participants (25-75 years old); an in-vivo dataset containing 10-min photoplethysmogram recordings from 57 healthy subjects at rest (18-39 or > 70 years old; 53% female); and an in-vivo dataset containing photoplethysmogram recordings collected for 4 weeks from a single subject, in daily life. The best-performing indices from the in-silico study (5/48 fiducial point analysis and 6/49 Symmetric Projection Attractor Reconstruction) were applied to the in-vivo datasets. Results Key fiducial point analysis and Symmetric Projection Attractor Reconstruction indices, which showed the greatest differences between groups, were found to be consistent across datasets. These indices were related to systolic augmentation, diastolic peak positioning and prominence, and waveform variability. Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques provided indices that supported the classification of age and physical activity, but not sex. Conclusions Both fiducial point analysis and Symmetric Projection Attractor Reconstruction techniques demonstrated utility in identifying cardiovascular differences between individuals and within an individual over time. Future research should investigate the potential utility of these techniques for extracting information on fitness and disease, to support healthcare-decision making.
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Affiliation(s)
- Aristide Jun Wen Mathieu
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital, London, UK
| | - Miquel Serna Pascual
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, Cambridgeshire, UK
| | - Maria Volovaya
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Jenny Venton
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Philip J Aston
- Department of Mathematics, University of Surrey, Guildford, UK
| | - Manasi Nandi
- School of Cancer and Pharmaceutical Science, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Jordi Alastruey
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, St Thomas' Hospital, London, UK
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Zhang F, Yang L, Wei J, Tian X. Non-Invasive Blood Pressure Tracking of Spontaneous Hypertension Rats Using an Electronic Nose. SENSORS (BASEL, SWITZERLAND) 2023; 24:238. [PMID: 38203100 PMCID: PMC10781391 DOI: 10.3390/s24010238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024]
Abstract
Traditional noninvasive blood pressure measurement methods in experimental animals are time consuming and difficult to operate, particularly for large numbers of animals. In this study, the possibility of sensing fecal odor to estimate the blood pressure status of spontaneous hypertension rats (SHRs) was explored with the aim of establishing a new method for non-invasive monitoring of blood pressure. The body weight and blood pressure of SHRs kept increasing with growth, and the odor information monitored using an E-nose varied with the blood pressure status, particularly for sensors S6 and S7. The fecal information was analyzed using principal component analysis, canonical discriminant analysis and multilayer perception neural networks (MLP) to discriminate SHRs from normal ones, with a 100% correct classification rate. For better prediction of blood pressure, the model built using multiple linear regression analysis, partial least squares regression analysis and multilayer perceptron neural network analysis were used, with coefficients of determination (R2) ranging from 0.8036 to 0.9926. Moreover, the best prediction model for blood pressure was established using MLP analysis with an R2¬ higher than 0.91. Thus, changes in blood pressure levels can be tracked non-invasively, and normotension can be distinguished from hypertension or even at different hypertension levels based on the odor information of rat feces, providing a foundation for non-invasive health monitoring. This work might provide potential instructions for functional food research aimed at lowering blood pressure.
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Affiliation(s)
- Fumei Zhang
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- Department of Medicine, Northwest Minzu University, Lanzhou 730124, China
| | - Lijing Yang
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
| | - Jia Wei
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
| | - Xiaojing Tian
- China-Malaysia National Joint Laboratory, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China; (F.Z.); (L.Y.); (J.W.)
- School of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China
- Gannan Yak Milk Research Institute, Gannan 747000, China
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Cano J, Bertomeu-González V, Fácila L, Hornero F, Alcaraz R, Rieta JJ. Improved Hypertension Risk Assessment with Photoplethysmographic Recordings Combining Deep Learning and Calibration. Bioengineering (Basel) 2023; 10:1439. [PMID: 38136030 PMCID: PMC10741001 DOI: 10.3390/bioengineering10121439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Hypertension, a primary risk factor for various cardiovascular diseases, is a global health concern. Early identification and effective management of hypertensive individuals are vital for reducing associated health risks. This study explores the potential of deep learning (DL) techniques, specifically GoogLeNet, ResNet-18, and ResNet-50, for discriminating between normotensive (NTS) and hypertensive (HTS) individuals using photoplethysmographic (PPG) recordings. The research assesses the impact of calibration at different time intervals between measurements, considering intervals less than 1 h, 1-6 h, 6-24 h, and over 24 h. Results indicate that calibration is most effective when measurements are closely spaced, with an accuracy exceeding 90% in all the DL strategies tested. For calibration intervals below 1 h, ResNet-18 achieved the highest accuracy (93.32%), sensitivity (84.09%), specificity (97.30%), and F1-score (88.36%). As the time interval between calibration and test measurements increased, classification performance gradually declined. For intervals exceeding 6 h, accuracy dropped below 81% but with all models maintaining accuracy above 71% even for intervals above 24 h. This study provides valuable insights into the feasibility of using DL for hypertension risk assessment, particularly through PPG recordings. It demonstrates that closely spaced calibration measurements can lead to highly accurate classification, emphasizing the potential for real-time applications. These findings may pave the way for advanced, non-invasive, and continuous blood pressure monitoring methods that are both efficient and reliable.
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Affiliation(s)
- Jesús Cano
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Vicente Bertomeu-González
- Cardiovascular Research Group, Clinical Medicine Department, Miguel Hernández University, 03202 Alicante, Spain;
| | - Lorenzo Fácila
- Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Fernando Hornero
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain;
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
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Evdochim L, Chiriac E, Avram M, Dobrescu L, Dobrescu D, Stanciu S, Halichidis S. Red Blood Cells' Area Deformation as the Origin of the Photoplethysmography Signal. SENSORS (BASEL, SWITZERLAND) 2023; 23:9515. [PMID: 38067889 PMCID: PMC10708758 DOI: 10.3390/s23239515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
The origin of the photoplethysmography (PPG) signal is a debatable topic, despite plausible models being addressed. One concern revolves around the correlation between the mechanical waveform's pulsatile nature and the associated biomechanism. The interface between these domains requires a clear mathematical or physical model that can explain physiological behavior. Describing the correct origin of the recorded optical waveform not only benefits the development of the next generation of biosensors but also defines novel health markers. In this study, the assumption of a pulsatile nature is based on the mechanism of blood microcirculation. At this level, two interconnected phenomena occur: variation in blood flow velocity through the capillary network and red blood cell (RBC) shape deformation. The latter effect was qualitatively investigated in synthetic capillaries to assess the experimental data needed for PPG model development. Erythrocytes passed through 10 µm and 6 µm microchannel widths with imposed velocities between 50 µm/s and 2000 µm/s, according to real scenarios. As a result, the length and area deformation of RBCs followed a logarithmic law function of the achieved traveling speeds. Applying radiometric expertise on top, mechanical-optical insights are obtained regarding PPG's pulsatile nature. The mathematical equations derived from experimental data correlate microcirculation physiologic with waveform behavior at a high confidence level. The transfer function between the biomechanics and the optical signal is primarily influenced by the vasomotor state, capillary network orientation, concentration, and deformation performance of erythrocytes.
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Affiliation(s)
- Lucian Evdochim
- Department of Electronic Devices, Circuits, and Architectures, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucharest, Romania; (L.D.); (D.D.)
| | - Eugen Chiriac
- National Institute for Research and Development in Microtechnologies—IMT Bucharest, 077190 Voluntari, Romania; (E.C.); (M.A.)
| | - Marioara Avram
- National Institute for Research and Development in Microtechnologies—IMT Bucharest, 077190 Voluntari, Romania; (E.C.); (M.A.)
| | - Lidia Dobrescu
- Department of Electronic Devices, Circuits, and Architectures, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucharest, Romania; (L.D.); (D.D.)
| | - Dragoș Dobrescu
- Department of Electronic Devices, Circuits, and Architectures, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 060042 Bucharest, Romania; (L.D.); (D.D.)
| | - Silviu Stanciu
- Laboratory of Cardiovascular Noninvasive Investigations, Dr. Carol Davila Central Military Emergency University Hospital, 010242 Bucharest, Romania;
| | - Stela Halichidis
- Department of Clinical Medical Disciplines, Faculty of Medicine, Ovidius University of Constanta, 900527 Constanta, Romania;
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Yilmaz G, Ong JL, Ling LH, Chee MWL. Insights into vascular physiology from sleep photoplethysmography. Sleep 2023; 46:zsad172. [PMID: 37379483 PMCID: PMC10566244 DOI: 10.1093/sleep/zsad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 05/19/2023] [Indexed: 06/30/2023] Open
Abstract
STUDY OBJECTIVES Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP). METHODS Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated. RESULTS BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency. CONCLUSIONS The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.
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Affiliation(s)
- Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Lieng-Hsi Ling
- Department of Cardiology, National University Heart Centre, National University Health System, Singapore and
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Yilmaz G, Lyu X, Ong JL, Ling LH, Penzel T, Yeo BTT, Chee MWL. Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7931. [PMID: 37765988 PMCID: PMC10537552 DOI: 10.3390/s23187931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/11/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.
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Affiliation(s)
- Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
| | - Xingyu Lyu
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
- Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
| | - Lieng Hsi Ling
- Department of Cardiology, National University Heart Centre Singapore, Singapore 119074, Singapore;
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany;
| | - B. T. Thomas Yeo
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
- Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117549, Singapore
- N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117549, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 117549, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02114, USA
| | - Michael W. L. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117549, Singapore; (G.Y.); (X.L.); (J.L.O.)
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Zhou Y, Tan Z, Liu Y, Cheng H. Fully convolutional neural network and PPG signal for arterial blood pressure waveform estimation. Physiol Meas 2023; 44:075007. [PMID: 37402386 DOI: 10.1088/1361-6579/ace414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 07/04/2023] [Indexed: 07/06/2023]
Abstract
Objective. The quality of the arterial blood pressure (ABP) waveform is crucial for predicting the value of blood pressure. The ABP waveform is predicted through experiments, and then Systolic blood pressure (SBP), Diastolic blood pressure, (DBP), and Mean arterial pressure (MAP) information are estimated from the ABP waveform.Approach. To ensure the quality of the predicted ABP waveform, this paper carefully designs the network structure, input signal, loss function, and structural parameters. A fully convolutional neural network (CNN) MultiResUNet3+ is used as the core architecture of ABP-MultiNet3+. In addition to performing Kalman filtering on the original photoplethysmogram (PPG) signal, its first-order derivative and second-order derivative signals are used as ABP-MultiNet3+ enter. The model's loss function uses a combination of mean absolute error (MAE) and means square error (MSE) loss to ensure that the predicted ABP waveform matches the reference waveform.Main results. The proposed ABP-MultiNet3+ model was tested on the public MIMIC II databases, MAE of MAP, DBP, and SBP was 1.88 mmHg, 3.11 mmHg, and 4.45 mmHg, respectively, indicating a small model error. It experiment fully meets the standards of the AAMI standard and obtains level A in the DBP and MAP prediction standard test under the BHS standard. For SBP prediction, it obtains level B in the BHS standard test. Although it does not reach level A, it has a certain improvement compared with the existing methods.Significance. The results show that this algorithm can achieve sleeveless blood pressure estimation, which may enable mobile medical devices to continuously monitor blood pressure and greatly reduce the harm caused by Cardiovascular disease (CVD).
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Affiliation(s)
- Yongan Zhou
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China
| | - Zhi Tan
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 100044, People's Republic of China
| | - Yuhong Liu
- College of Pulmonary & Critical Care Medicine, 8th Medical Center, Chinese PLA General Hospital, People's Republic of China
- Beijing IROT Key Laboratory, People's Republic of China
| | - Haibo Cheng
- Jiangsu Future Network Group Co., Ltd, People's Republic of China
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11
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Xing X, Huang R, Hao L, Jiang C, Dong WF. Temporal complexity in photoplethysmography and its influence on blood pressure. Front Physiol 2023; 14:1187561. [PMID: 37745247 PMCID: PMC10513039 DOI: 10.3389/fphys.2023.1187561] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/18/2023] [Indexed: 09/26/2023] Open
Abstract
Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.
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Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Rui Huang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Liling Hao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chenyu Jiang
- Jinan Guoke Medical Technology Development Co. Ltd., Jinan, China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
- Suzhou GK Medtech Science and Technology Development (Group) Co. Ltd., Suzhou, China
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12
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Mahardika T NQ, Fuadah YN, Jeong DU, Lim KM. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM. Diagnostics (Basel) 2023; 13:2566. [PMID: 37568929 PMCID: PMC10417316 DOI: 10.3390/diagnostics13152566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients' health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN-LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN-LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN-LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.
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Affiliation(s)
- Nurul Qashri Mahardika T
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
| | - Yunendah Nur Fuadah
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
- School of Electrical Engineering, Telkom University, Bandung 40257, Indonesia
| | - Da Un Jeong
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
| | - Ki Moo Lim
- Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea; (N.Q.M.T.); (Y.N.F.); (D.U.J.)
- Computational Medicine Lab, Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Gyeongbuk, Republic of Korea
- Meta Heart Co., Ltd., Gumi 39177, Gyeongbuk, Republic of Korea
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13
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Joung J, Jung CW, Lee HC, Chae MJ, Kim HS, Park J, Shin WY, Kim C, Lee M, Choi C. Continuous cuffless blood pressure monitoring using photoplethysmography-based PPG2BP-net for high intrasubject blood pressure variations. Sci Rep 2023; 13:8605. [PMID: 37244974 DOI: 10.1038/s41598-023-35492-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/18/2023] [Indexed: 05/29/2023] Open
Abstract
Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately [Formula: see text], [Formula: see text], and [Formula: see text] of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel 'standard deviation of subject-calibration centring (SDS)' metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of [Formula: see text] and [Formula: see text] for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.
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Affiliation(s)
- Jingon Joung
- Department of Electrical and Electronics Engineering, Chung-Ang University, Seoul, 06974, South Korea.
| | - Chul-Woo Jung
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, 03080, South Korea
| | - Moon-Jung Chae
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Hae-Sung Kim
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Jonghun Park
- Department of Industrial Engineering, Seoul National University, Seoul, 08826, South Korea
| | - Won-Yong Shin
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, 03722, South Korea
- Pohang University of Science and Technology (POSTECH) (Artificial Intelligence), Pohang, 37673, South Korea
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14
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Rovas G, Bikia V, Stergiopulos N. Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography. Bioengineering (Basel) 2023; 10:bioengineering10040460. [PMID: 37106647 PMCID: PMC10136360 DOI: 10.3390/bioengineering10040460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/06/2023] [Accepted: 04/08/2023] [Indexed: 04/29/2023] Open
Abstract
Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomechanical processes that influence the reflective PPG signal. We developed a theoretical model to describe the dependence of reflected light on the pressure, flow rate, and the hemorheological properties of erythrocytes. To verify the theory, we designed a silicone model of a human radial artery, inserted it in a mock circulatory circuit filled with porcine blood, and imposed static and pulsatile flow conditions. We found a positive, linear relationship between the pressure and the PPG and a negative, non-linear relationship, of comparable magnitude, between the flow and the PPG. Additionally, we quantified the effects of the erythrocyte disorientation and aggregation. The theoretical model based on pressure and flow rate yielded more accurate predictions, compared to the model using pressure alone. Our results indicate that the PPG waveform is not a suitable surrogate for intraluminal pressure and that flow rate significantly affects PPG. Further validation of the proposed methodology in vivo could enable the non-invasive estimation of arterial pressure from PPG and increase the accuracy of health-monitoring devices.
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Affiliation(s)
- Georgios Rovas
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Vasiliki Bikia
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Nikolaos Stergiopulos
- Laboratory of Hemodynamics and Cardiovascular Technology, Institute of Bioengineering, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
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15
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González S, Hsieh WT, Chen TPC. A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram. Sci Data 2023; 10:149. [PMID: 36944668 PMCID: PMC10030661 DOI: 10.1038/s41597-023-02020-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 02/14/2023] [Indexed: 03/23/2023] Open
Abstract
Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.
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16
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Rajput V, Mulay P, Pandya S, Mahajan C, Deshpande R. Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model. ACTA INFORMATICA PRAGENSIA 2023. [DOI: 10.18267/j.aip.209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023] Open
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17
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Wang CF, Wang TY, Kuo PH, Wang HL, Li SZ, Lin CM, Chan SC, Liu TY, Lo YC, Lin SH, Chen YY. Upper-Arm Photoplethysmographic Sensor with One-Time Calibration for Long-Term Blood Pressure Monitoring. BIOSENSORS 2023; 13:321. [PMID: 36979533 PMCID: PMC10046397 DOI: 10.3390/bios13030321] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/16/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Wearable cuffless photoplethysmographic blood pressure monitors have garnered widespread attention in recent years; however, the long-term performance values of these devices are questionable. Most cuffless blood pressure monitors require initial baseline calibration and regular recalibrations with a cuffed blood pressure monitor to ensure accurate blood pressure estimation, and their estimation accuracy may vary over time if left uncalibrated. Therefore, this study assessed the accuracy and long-term performance of an upper-arm, cuffless photoplethysmographic blood pressure monitor according to the ISO 81060-2 standard. This device was based on a nonlinear machine-learning model architecture with a fine-tuning optimized method. The blood pressure measurement protocol followed a validation procedure according to the standard, with an additional four weekly blood pressure measurements over a 1-month period, to assess the long-term performance values of the upper-arm, cuffless photoplethysmographic blood pressure monitor. The results showed that the photoplethysmographic signals obtained from the upper arm had better qualities when compared with those measured from the wrist. When compared with the cuffed blood pressure monitor, the means ± standard deviations of the difference in BP at week 1 (baseline) were -1.36 ± 7.24 and -2.11 ± 5.71 mmHg for systolic and diastolic blood pressure, respectively, which met the first criterion of ≤5 ± ≤8.0 mmHg and met the second criterion of a systolic blood pressure ≤ 6.89 mmHg and a diastolic blood pressure ≤ 6.84 mmHg. The differences in the uncalibrated blood pressure values between the test and reference blood pressure monitors measured from week 2 to week 5 remained stable and met both criteria 1 and 2 of the ISO 81060-2 standard. The upper-arm, cuffless photoplethysmographic blood pressure monitor in this study generated high-quality photoplethysmographic signals with satisfactory accuracy at both initial calibration and 1-month follow-ups. This device could be a convenient and practical tool to continuously measure blood pressure over long periods of time.
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Affiliation(s)
- Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Ting-Yun Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
- Material and Chemical Research Laboratories, Industrial Technology Research Institute, No. 195, Sec. 4, Chunghsing Rd., Hsinchu 310401, Taiwan
| | - Pei-Hsin Kuo
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu chi Medical Foundation, No. 707, Sec. 3, Zhongyang Rd., Hualien 970473, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Han-Lin Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
| | - Shih-Zhang Li
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
| | - Chia-Ming Lin
- Microlife Corporation, 9F, No. 431, Ruiguang Rd., Taipei 114063, Taiwan
| | - Shih-Chieh Chan
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
- Microlife Corporation, 9F, No. 431, Ruiguang Rd., Taipei 114063, Taiwan
| | - Tzu-Yu Liu
- Material and Chemical Research Laboratories, Industrial Technology Research Institute, No. 195, Sec. 4, Chunghsing Rd., Hsinchu 310401, Taiwan
| | - Yu-Chun Lo
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, No. 250, Wu-Xing St., Taipei 11031, Taiwan
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu chi Medical Foundation, No. 707, Sec. 3, Zhongyang Rd., Hualien 970473, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Taipei 112304, Taiwan
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, No. 250, Wu-Xing St., Taipei 11031, Taiwan
- Medical Device Innovation and Translation Center, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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18
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Aguet C, Jorge J, Van Zaen J, Proença M, Bonnier G, Frossard P, Lemay M. Blood pressure monitoring during anesthesia induction using PPG morphology features and machine learning. PLoS One 2023; 18:e0279419. [PMID: 36735652 PMCID: PMC9897516 DOI: 10.1371/journal.pone.0279419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 12/06/2022] [Indexed: 02/04/2023] Open
Abstract
Blood pressure (BP) is a crucial biomarker giving valuable information regarding cardiovascular diseases but requires accurate continuous monitoring to maximize its value. In the effort of developing non-invasive, non-occlusive and continuous BP monitoring devices, photoplethysmography (PPG) has recently gained interest. Researchers have attempted to estimate BP based on the analysis of PPG waveform morphology, with promising results, yet often validated on a small number of subjects with moderate BP variations. This work presents an accurate BP estimator based on PPG morphology features. The method first uses a clinically-validated algorithm (oBPM®) to perform signal preprocessing and extraction of physiological features. A subset of features that best reflects BP changes is automatically identified by Lasso regression, and a feature relevance analysis is conducted. Three machine learning (ML) methods are then investigated to translate this subset of features into systolic BP (SBP) and diastolic BP (DBP) estimates; namely Lasso regression, support vector regression and Gaussian process regression. The accuracy of absolute BP estimates and trending ability are evaluated. Such an approach considerably improves the performance for SBP estimation over previous oBPM® technology, with a reduction in the standard deviation of the error of over 20%. Furthermore, rapid BP changes assessed by the PPG-based approach demonstrates concordance rate over 99% with the invasive reference. Altogether, the results confirm that PPG morphology features can be combined with ML methods to accurately track BP variations generated during anesthesia induction. They also reinforce the importance of adding a calibration measure to obtain an absolute BP estimate.
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Affiliation(s)
- Clémentine Aguet
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
- Signal Processing Laboratory (LTS4), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- * E-mail:
| | - João Jorge
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Jérôme Van Zaen
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Martin Proença
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Guillaume Bonnier
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
| | - Pascal Frossard
- Signal Processing Laboratory (LTS4), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mathieu Lemay
- Signal Processing Group, Swiss Center for Electronics and Microtechnology (CSEM), Neuchâtel, Switzerland
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19
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Hu X, Yin S, Zhang X, Menon C, Fang C, Chen Z, Elgendi M, Liang Y. Blood pressure stratification using photoplethysmography and light gradient boosting machine. Front Physiol 2023; 14:1072273. [PMID: 36891146 PMCID: PMC9986584 DOI: 10.3389/fphys.2023.1072273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.
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Affiliation(s)
- Xudong Hu
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Shimin Yin
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Xizhuang Zhang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China
| | - Carlo Menon
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Cheng Fang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
| | - Zhencheng Chen
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Lab, ETH Zurich, Zurich, Switzerland
| | - Yongbo Liang
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China.,Guangxi Colleges and Universities Key Laboratory of Biomedical Sensors and Intelligent Instruments, Guilin, China.,Guangxi Engineering Technology Research Center of Human Physiological Information Noninvasive Detection, Guilin, China
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20
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Mahmud S, Ibtehaz N, Khandakar A, Sohel Rahman M, JR. Gonzales A, Rahman T, Shafayet Hossain M, Sakib Abrar Hossain M, Ahasan Atick Faisal M, Fuad Abir F, Musharavati F, E. H. Chowdhury M. NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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21
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Meng Z, Yang X, Liu X, Wang D, Han X. Non-invasive blood pressure estimation combining deep neural networks with pre-training and partial fine-tuning. Physiol Meas 2022; 43. [PMID: 36301705 DOI: 10.1088/1361-6579/ac9d7f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 10/25/2022] [Indexed: 02/07/2023]
Abstract
Objective. Daily blood pressure (BP) monitoring is essential since BP levels can reflect the functions of heart pumping and vasoconstriction. Although various neural network-based BP estimate approaches have been proposed, they have certain practical shortcomings, such as low estimation accuracy and poor model generalization. Based on the strategy of pre-training and partial fine-tuning, this work proposes a non-invasive method for BP estimation using the photoplethysmography (PPG) signal.Approach. To learn the PPG-BP relationship, the deep convolutional bidirectional recurrent neural network (DC-Bi-RNN) was pre-trained with data from the public medical information mark for intensive care (MIMIC III) database. A tiny quantity of data from the target subject was used to fine-tune the specific layers of the pre-trained model to learn more individual-specific information to achieve highly accurate BP estimation.Main results.The mean absolute error and the Pearson correlation coefficient (r) of the proposed algorithm are 3.21 mmHg and 0.919 for systolic BP, and 1.80 mmHg and 0.898 for diastolic BP (DBP). The experimental results show that our method outperforms other methods and meets the requirements of the Association for the Advancement of Medical Instrumentation standard, and received an A grade according to the British Hypertension Society standard.Significance.The proposed method applies the strategy of pre-training and partial fine-tuning to BP estimation and verifies its effectiveness in improving the accuracy of non-invasive BP estimation.
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Affiliation(s)
- Ziyan Meng
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuezhi Yang
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuenan Liu
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Dingliang Wang
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
| | - Xuesong Han
- School of Computer and Information, Hefei University of Technology, Hefei 230009, People's Republic of China.,Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei 230009, People's Republic of China
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22
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Knight S, Lipoth J, Namvari M, Gu C, Hedayati Ch. M, Syed-Abdul S, Spiteri RJ. The Accuracy of Wearable Photoplethysmography Sensors for Telehealth Monitoring: A Scoping Review. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Sheida Knight
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jessica Lipoth
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Mina Namvari
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Carol Gu
- Center for Bio-Integrated Electronics at Northwestern University, Evanston, Illinois, USA
| | | | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan
| | - Raymond J. Spiteri
- Department of Computer Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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23
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Tang Q, Chen Z, Ward R, Menon C, Elgendi M. Subject-Based Model for Reconstructing Arterial Blood Pressure from Photoplethysmogram. Bioengineering (Basel) 2022; 9:bioengineering9080402. [PMID: 36004927 PMCID: PMC9404925 DOI: 10.3390/bioengineering9080402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022] Open
Abstract
The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.
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Affiliation(s)
- Qunfeng Tang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Zhencheng Chen
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China
- Correspondence: (Z.C.); (M.E.)
| | - Rabab Ward
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
| | - Mohamed Elgendi
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z2, Canada
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, 8008 Zurich, Switzerland
- Correspondence: (Z.C.); (M.E.)
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24
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Cano J, Fácila L, Gracia-Baena JM, Zangróniz R, Alcaraz R, Rieta JJ. The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography. BIOSENSORS 2022; 12:bios12050289. [PMID: 35624590 PMCID: PMC9138834 DOI: 10.3390/bios12050289] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023]
Abstract
The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.
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Affiliation(s)
- Jesús Cano
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
| | - Lorenzo Fácila
- Cardiology Department, General University Hospital Consortium of Valencia, 46014 Valencia, Spain;
| | - Juan M. Gracia-Baena
- Cardiovascular Surgery Department, Hospital Clínico Universitario de Valencia, 46010 Valencia, Spain;
| | - Roberto Zangróniz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - Raúl Alcaraz
- Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, 16071 Cuenca, Spain; (R.Z.); (R.A.)
| | - José J. Rieta
- BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain;
- Correspondence:
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25
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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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26
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Almarshad MA, Islam MS, Al-Ahmadi S, BaHammam AS. Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review. Healthcare (Basel) 2022; 10:healthcare10030547. [PMID: 35327025 PMCID: PMC8950880 DOI: 10.3390/healthcare10030547] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 02/04/2023] Open
Abstract
Recent research indicates that Photoplethysmography (PPG) signals carry more information than oxygen saturation level (SpO2) and can be utilized for affordable, fast, and noninvasive healthcare applications. All these encourage the researchers to estimate its feasibility as an alternative to many expansive, time-wasting, and invasive methods. This systematic review discusses the current literature on diagnostic features of PPG signal and their applications that might present a potential venue to be adapted into many health and fitness aspects of human life. The research methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020. To this aim, papers from 1981 to date are reviewed and categorized in terms of the healthcare application domain. Along with consolidated research areas, recent topics that are growing in popularity are also discovered. We also highlight the potential impact of using PPG signals on an individual’s quality of life and public health. The state-of-the-art studies suggest that in the years to come PPG wearables will become pervasive in many fields of medical practices, and the main domains include cardiology, respiratory, neurology, and fitness. Main operation challenges, including performance and robustness obstacles, are identified.
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Affiliation(s)
- Malak Abdullah Almarshad
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
- Computer Science Department, College of Computer and Information Sciences, Al-Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia
- Correspondence:
| | - Md Saiful Islam
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Saad Al-Ahmadi
- Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; (M.S.I.); (S.A.-A.)
| | - Ahmed S. BaHammam
- The University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University, Riyadh 11324, Saudi Arabia;
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27
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Chen JW, Huang HK, Fang YT, Lin YT, Li SZ, Chen BW, Lo YC, Chen PC, Wang CF, Chen YY. A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography. SENSORS 2022; 22:s22051873. [PMID: 35271020 PMCID: PMC8914760 DOI: 10.3390/s22051873] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/07/2022] [Accepted: 02/25/2022] [Indexed: 12/05/2022]
Abstract
Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.
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Affiliation(s)
- Jia-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
| | - Hsin-Kai Huang
- Department of Cardiology, Ten-Chan General Hospital (Chung Li), Taoyuan 32043, Taiwan;
| | - Yu-Ting Fang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
- Food and Drug Administration, Ministry of Health and Welfare, Taipei 11561, Taiwan
| | - Yen-Ting Lin
- Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan 33004, Taiwan;
| | - Shih-Zhang Li
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
| | - Yu-Chun Lo
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Po-Chuan Chen
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
- Correspondence: (C.-F.W.); (Y.-Y.C.)
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (J.-W.C.); (Y.-T.F.); (S.-Z.L.); (B.-W.C.)
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Correspondence: (C.-F.W.); (Y.-Y.C.)
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28
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Cuffless Blood Pressure Measurement Using Linear and Nonlinear Optimized Feature Selection. Diagnostics (Basel) 2022; 12:diagnostics12020408. [PMID: 35204499 PMCID: PMC8870879 DOI: 10.3390/diagnostics12020408] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 02/04/2023] Open
Abstract
The cuffless blood pressure (BP) measurement allows for frequent measurement without discomfort to the patient compared to the cuff inflation measurement. With the availability of a large dataset containing physiological waveforms, now it is possible to use them through different learning algorithms to produce a relationship with changes in BP. In this paper, a novel cuffless noninvasive blood pressure measurement technique has been proposed using optimized features from electrocardiogram and photoplethysmography based on multivariate symmetric uncertainty (MSU). The technique is an improvement over other contemporary methods due to the inclusion of feature optimization depending on both linear and nonlinear relationships with the change of blood pressure. MSU has been used as a selection criterion with algorithms such as the fast correlation and ReliefF algorithms followed by the penalty-based regression technique to make sure the features have maximum relevance as well as minimum redundancy. The result from the technique was compared with the performance of similar techniques using the MIMIC-II dataset. After training and testing, the root mean square error (RMSE) comes as 5.28 mmHg for systolic BP and 5.98 mmHg for diastolic BP. In addition, in terms of mean absolute error, the result improved to 4.27 mmHg for SBP and 5.01 for DBP compared to recent cuffless BP measurement techniques which have used substantially large datasets and feature optimization. According to the British Hypertension Society Standard (BHS), our proposed technique achieved at least grade B in all cumulative criteria for cuffless BP measurement.
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29
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Wang W, Mohseni P, Kilgore KL, Najafizadeh L. Cuff-less Blood Pressure Estimation from Photoplethysmography via Visibility Graph and Transfer Learning. IEEE J Biomed Health Inform 2021; 26:2075-2085. [PMID: 34784289 DOI: 10.1109/jbhi.2021.3128383] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents a new solution that enables the use of transfer learning for cuff-less blood pressure (BP) monitoring via short duration of photoplethysmogram (PPG). The proposed method estimates BP with low computational budget by 1) creating images from segments of PPG via visibility graph (VG) that preserves the temporal information of the PPG waveform, 2) using pre-trained deep convolutional neural network (CNN) to extract feature vectors from VG images, and 3) solving for the weights and bias between the feature vectors and the reference BPs with ridge regression. Using the University of California Irvine (UCI) database consisting of 348 records, the proposed method achieves a best error performance of 0.008.46 mmHg for systolic blood pressure (SBP), and -0.045.36 mmHg for diastolic blood pressure (DBP), respectively, in terms of the mean error (ME) and the standard deviation (SD) of error, ranking grade B for SBP and grade A for DBP under the British Hypertension Society (BHS) protocol. Our novel data-driven method offers a computationally-efficient end-to-end solution for rapid and user-friendly cuff-less PPG-based BP estimation.
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30
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Steinman J, Barszczyk A, Sun HS, Lee K, Feng ZP. Smartphones and Video Cameras: Future Methods for Blood Pressure Measurement. Front Digit Health 2021; 3:770096. [PMID: 34870272 PMCID: PMC8633391 DOI: 10.3389/fdgth.2021.770096] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/15/2021] [Indexed: 11/24/2022] Open
Abstract
Regular blood pressure (BP) monitoring enables earlier detection of hypertension and reduces cardiovascular disease. Cuff-based BP measurements require equipment that is inconvenient for some individuals and deters regular home-based monitoring. Since smartphones contain sensors such as video cameras that detect arterial pulsations, they could also be used to assess cardiovascular health. Researchers have developed a variety of image processing and machine learning techniques for predicting BP via smartphone or video camera. This review highlights research behind smartphone and video camera methods for measuring BP. These methods may in future be used at home or in clinics, but must be tested over a larger range of BP and lighting conditions. The review concludes with a discussion of the advantages of the various techniques, their potential clinical applications, and future directions and challenges. Video cameras may potentially measure multiple cardiovascular metrics including and beyond BP, reducing the risk of cardiovascular disease.
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Affiliation(s)
- Joe Steinman
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Andrew Barszczyk
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Dr. Eric Jackman Institute of Child Study, University of Toronto, Toronto, ON, Canada
| | - Hong-Shuo Sun
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Kang Lee
- Dr. Eric Jackman Institute of Child Study, University of Toronto, Toronto, ON, Canada
| | - Zhong-Ping Feng
- Department of Physiology, University of Toronto, Toronto, ON, Canada
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31
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Xing X, Ma Z, Xu S, Zhang M, Zhao W, Song M, Dong WF. Blood pressure assessment with in-ear photoplethysmography. Physiol Meas 2021; 42. [PMID: 34571491 DOI: 10.1088/1361-6579/ac2a71] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/27/2021] [Indexed: 11/11/2022]
Abstract
Objective. In this study, we aimed to estimate blood pressure (BP) from in-ear photoplethysmography (PPG). This novel implementation provided an unobtrusive and steady way of recording PPG, whereas previous PPG measurements were mostly performed at the wrist, finger, or earlobe.Methods. The time between forward and reflected PPG waves was very short at the ear site. To minimize errors introduced by feature extraction, a multi-Gaussian decomposition of in-ear PPG was performed. Both hand-crafted and whole-based features were extracted and the best combination of features was selected using a backward-search wrapper method and evaluated by the Akaike information criteria. Hemodynamic parameters such as compliance and inertance were estimated from a four-element Windkessel (WK4) model, which was used to pre-classify PPG signals and generate different BP estimation algorithms. Calibration was done by using previous measurements from the same class. To validate this novel approach, 53 subjects were recruited for a one-month follow-up study, and 17 subjects were recruited for a two-month follow-up study. Calibrated systolic BP estimation accuracy was significantly improved with inertance-based pre-classification, while diastolic BP showed less improvement.Results. With proper feature selection, pre-classification and calibration, we have achieved a mean absolute error of 5.35 mmHg for SBP estimation, compared to 6.16 mmHg if no pre-classification was carried out. The performance did not deteriorate in two months, showing a decent BP trend-tracking ability.Conclusion. The study demonstrated the feasibility of in-ear PPG to reliably measure BP, which represents an important technological advancement in terms of unobtrusiveness and steadiness.
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Affiliation(s)
- Xiaoman Xing
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Sciences and Technology of China, Suzhou, Jiangsu, People's Republic of China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, People's Republic of China
| | - Zhimin Ma
- The Affiliated Suzhou Science &Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, People's Republic of China
| | - Shengkai Xu
- The Affiliated Suzhou Science &Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, People's Republic of China
| | - Mingyou Zhang
- The First Hospital of Jilin University, Changchun, Jilin, People's Republic of China
| | - Wei Zhao
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Mingxuan Song
- Jinan Guoke Medical Technology Development Co., Ltd, Shandong, People's Republic of China
| | - Wen-Fei Dong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, People's Republic of China
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32
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Wang W, Mohseni P, Kilgore K, Najafizadeh L. Cuff-Less Blood Pressure Estimation via Small Convolutional Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1031-1034. [PMID: 34891464 DOI: 10.1109/embc46164.2021.9630557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Deep learning-based cuff-less blood pressure (BP) estimation methods have recently gained increased attention as they can provide accurate BP estimation with only one physiological signal as input. In this paper, we present a simple and effective method for cuff-less BP estimation by training a small-scale convolutional neural network (CNN), modified from LeNet-5, with images created from short segments of the photoplethysmogram (PPG) signal via visibility graph (VG). Results show that the trained modified LeNet-5 model achieves an error performance of 0.184±7.457 mmHg for the systolic BP (SBP), and 0.343±4.065 mmHg for the diastolic BP (DBP) in terms of the mean error (ME) and the standard deviation (SD) of error between the estimated and reference BP. Both the SBP and the DBP accuracy rank grade A under the British Hypertension Society (BHS) protocol, demonstrating that our proposed method is an accurate way for cuff-less BP estimation.
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33
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Aguet C, Zaen JV, Jorge J, Proenca M, Bonnier G, Frossard P, Lemay M. Feature Learning for Blood Pressure Estimation from Photoplethysmography. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:463-466. [PMID: 34891333 DOI: 10.1109/embc46164.2021.9630665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Blood pressure (BP) is an important indicator for prevention and management of cardiovascular diseases. Alongside the improvement in sensors and wearables, photoplethysmography (PPG) appears to be a promising technology for continuous, non-invasive and cuffless BP monitoring. Previous attempts mainly focused on features extracted from the pulse morphology. In this paper, we propose to remove the feature engineering step and automatically generate features from an ensemble average (EA) PPG pulse and its derivatives, using convolutional neural network and a calibration measurement. We used the large VitalDB dataset to accurately evaluate the generalization capability of the proposed model. The model achieved mean errors of -0.24 ± 11.56 mmHg for SBP and -0.5 ± 6.52 mmHg for DBP. We observed a considerable reduction in error standard deviation of above 40% compared to the control case, which assumes no BP variation. Altogether, these results highlight the capability to model the dependency between PPG and BP.
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Li Z, He W. A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model. SENSORS (BASEL, SWITZERLAND) 2021; 21:7207. [PMID: 34770514 PMCID: PMC8587576 DOI: 10.3390/s21217207] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/13/2021] [Accepted: 10/27/2021] [Indexed: 11/27/2022]
Abstract
Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
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Affiliation(s)
| | - Wei He
- State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044, China;
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Schukraft S, Boukhayma A, Cook S, Caizzone A. Remote Blood Pressure Monitoring With a Wearable Photoplethysmographic Device (Senbiosys): Protocol for a Single-Center Prospective Clinical Trial. JMIR Res Protoc 2021; 10:e30051. [PMID: 34617912 PMCID: PMC8532013 DOI: 10.2196/30051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/05/2021] [Accepted: 07/26/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Wearable devices can provide user-friendly, accurate, and continuous blood pressure (BP) monitoring to assess patients' vital signs and achieve remote patient management. Remote BP monitoring can substantially improve BP control. The newest cuffless BP monitoring devices have emerged in patient care using photoplethysmography. OBJECTIVE The Senbiosys trial aims to compare BP measurements of a new device capturing a photoplethysmography signal on the finger versus invasive measurements performed in patients with an arterial catheter in the intensive care unit (ICU) or referred for a coronarography at the Hospital of Fribourg. METHODS The Senbiosys study is a single-center, single-arm, prospective trial. The study population consists of adult patients undergoing coronarography or patients in the ICU with an arterial catheter in place. This study will enroll 35 adult patients, including 25 patients addressed for a coronarography and 10 patients in the ICU. The primary outcome is the assessment of mean bias (95% CI) for systolic BP, diastolic BP, and mean BP between noninvasive and invasive BP measurements. Secondary outcomes include a reliability index (Qualification Index) for BP epochs and count of qualified epochs. RESULTS Patient recruitment started in June 2021. Results are expected to be published by December 2021. CONCLUSIONS The findings of the Senbiosys trial are expected to improve remote BP monitoring. The diagnosis and treatment of hypertension should benefit from these advancements. TRIAL REGISTRATION ClinicalTrials.gov NCT04379986; https://clinicaltrials.gov/ct2/show/NCT04379986. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/30051.
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Affiliation(s)
- Sara Schukraft
- Department of Cardiology, University and Hospital Fribourg, Fribourg, Switzerland
| | - Assim Boukhayma
- Microcity Pôle d'innovation Neuchâtel, Neuchâtel, Switzerland
| | - Stéphane Cook
- Department of Cardiology, University and Hospital Fribourg, Fribourg, Switzerland
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Cheng J, Xu Y, Song R, Liu Y, Li C, Chen X. Prediction of arterial blood pressure waveforms from photoplethysmogram signals via fully convolutional neural networks. Comput Biol Med 2021; 138:104877. [PMID: 34571436 DOI: 10.1016/j.compbiomed.2021.104877] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 01/16/2023]
Abstract
Cardiovascular disease (CVD) is one of the most serious diseases threatening human health. Arterial blood pressure (ABP) waveforms, containing vivid cardiovascular information, are of great significance for the diagnosis and the prevention of CVD. This paper proposes a deep learning model, named ABP-Net, to transform photoplethysmogram (PPG) signals into ABP waveforms that contain vital physiological information related to cardiovascular systems. In order to guarantee the quality of the predicted ABP waveforms, the structure of the network, the input signals and the loss functions are carefully designed. Specifically, a Wave-U-Net, one kind of fully convolutional neural networks (CNN), is taken as the core architecture of the ABP-Net. Besides the original PPG signals, its first derivative and second derivative signals are all utilized as the inputs of the ABP-Net. Additionally, the maximal absolute loss, accompany with the mean squared error loss is employed to ensure the match of the predicted ABP waveform with the reference one. The performance of the proposed ABP network is tested on the public MIMIC II database both in subject-dependent and subject-independent manners. Both results verify the superior performance of the proposed model over those existing methods accordingly. The mean absolute error (MAE) and the root-mean-square error (RMSE) between the predicted waveforms via the ABP-Net and the reference ones are 3.20 mmHg and 4.38 mmHg during the subject-dependent experiments while those are 5.57 mmHg and 7.15 mmHg during the subject-independent experiments. Benefiting from the predicted high-quality ABP waveforms, more ABP related physiological parameters can be better obtained, which effectively expands the application scope of PPG devices.
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Affiliation(s)
- Juan Cheng
- Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China; Anhui Province Key Laboratory of Measuring Theory and Precision Instrument, Hefei University of Technology, Hefei, 230009, China
| | - Yufei Xu
- Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Rencheng Song
- Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China.
| | - Yu Liu
- Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Chang Li
- Department of Biomedical Engineering, Hefei University of Technology, Hefei, 230009, China
| | - Xun Chen
- Department of Electronic Engineering & Information Science, University of Science and Technology of China, Hefei, 230026, China
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Deep generative model with domain adversarial training for predicting arterial blood pressure waveform from photoplethysmogram signal. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102972] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Harfiya LN, Chang CC, Li YH. Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation. SENSORS (BASEL, SWITZERLAND) 2021; 21:2952. [PMID: 33922447 PMCID: PMC8122812 DOI: 10.3390/s21092952] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 11/16/2022]
Abstract
Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.
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Affiliation(s)
- Latifa Nabila Harfiya
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan;
| | - Ching-Chun Chang
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;
| | - Yung-Hui Li
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan;
- AI Research Center, Hon Hai Research Institute, Taipei 114699, Taiwan
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Aguirre N, Grall-Maës E, Cymberknop LJ, Armentano RL. Blood Pressure Morphology Assessment from Photoplethysmogram and Demographic Information Using Deep Learning with Attention Mechanism. SENSORS 2021; 21:s21062167. [PMID: 33808925 PMCID: PMC8003691 DOI: 10.3390/s21062167] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/10/2021] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
Arterial blood pressure (ABP) is an important vital sign from which it can be extracted valuable information about the subject's health. After studying its morphology it is possible to diagnose cardiovascular diseases such as hypertension, so ABP routine control is recommended. The most common method of controlling ABP is the cuff-based method, from which it is obtained only the systolic and diastolic blood pressure (SBP and DBP, respectively). This paper proposes a cuff-free method to estimate the morphology of the average ABP pulse (ABPM¯) through a deep learning model based on a seq2seq architecture with attention mechanism. It only needs raw photoplethysmogram signals (PPG) from the finger and includes the capacity to integrate both categorical and continuous demographic information (DI). The experiments were performed on more than 1100 subjects from the MIMIC database for which their corresponding age and gender were consulted. Without allowing the use of data from the same subjects to train and test, the mean absolute errors (MAE) were 6.57 ± 0.20 and 14.39 ± 0.42 mmHg for DBP and SBP, respectively. For ABPM¯, R correlation coefficient and the MAE were 0.98 ± 0.001 and 8.89 ± 0.10 mmHg. In summary, this methodology is capable of transforming PPG into an ABP pulse, which obtains better results when DI of the subjects is used, potentially useful in times when wireless devices are becoming more popular.
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Affiliation(s)
- Nicolas Aguirre
- GIBIO, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Ciudad Autónoma Buenos Aires C1179AAQ, Argentina; (L.J.C.); (R.L.A.)
- LIST3N, Université de Technologie de Troyes, 10004 Troyes, France;
- Correspondence:
| | - Edith Grall-Maës
- LIST3N, Université de Technologie de Troyes, 10004 Troyes, France;
| | - Leandro J. Cymberknop
- GIBIO, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Ciudad Autónoma Buenos Aires C1179AAQ, Argentina; (L.J.C.); (R.L.A.)
| | - Ricardo L. Armentano
- GIBIO, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional, Ciudad Autónoma Buenos Aires C1179AAQ, Argentina; (L.J.C.); (R.L.A.)
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Athaya T, Choi S. An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach. SENSORS (BASEL, SWITZERLAND) 2021; 21:1867. [PMID: 33800106 PMCID: PMC7962188 DOI: 10.3390/s21051867] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 01/20/2023]
Abstract
Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson's correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.
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Affiliation(s)
| | - Sunwoong Choi
- School of Electrical Engineering, Kookimin University, Seoul 02707, Korea;
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41
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Mena LJ, Félix VG, Ostos R, González AJ, Martínez-Peláez R, Melgarejo JD, Maestre GE. Mobile Personal Health Care System for Noninvasive, Pervasive, and Continuous Blood Pressure Monitoring: Development and Usability Study. JMIR Mhealth Uhealth 2020; 8:e18012. [PMID: 32459642 PMCID: PMC7400045 DOI: 10.2196/18012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 04/11/2020] [Accepted: 04/26/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Smartphone-based blood pressure (BP) monitoring using photoplethysmography (PPG) technology has emerged as a promising approach to empower users with self-monitoring for effective diagnosis and control of hypertension. OBJECTIVE This study aimed to develop a mobile personal health care system for noninvasive, pervasive, and continuous estimation of BP level and variability, which is user friendly for elderly people. METHODS The proposed approach was integrated by a self-designed cuffless, calibration-free, wireless, and wearable PPG-only sensor and a native purposely designed smartphone app using multilayer perceptron machine learning techniques from raw signals. We performed a development and usability study with three older adults (mean age 61.3 years, SD 1.5 years; 66% women) to test the usability and accuracy of the smartphone-based BP monitor. RESULTS The employed artificial neural network model had good average accuracy (>90%) and very strong correlation (>0.90) (P<.001) for predicting the reference BP values of our validation sample (n=150). Bland-Altman plots showed that most of the errors for BP prediction were less than 10 mmHg. However, according to the Association for the Advancement of Medical Instrumentation and British Hypertension Society standards, only diastolic blood pressure prediction met the clinically accepted accuracy thresholds. CONCLUSIONS With further development and validation, the proposed system could provide a cost-effective strategy to improve the quality and coverage of health care, particularly in rural zones, areas lacking physicians, and areas with solitary elderly populations.
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Affiliation(s)
- Luis J Mena
- Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan, Mexico
| | - Vanessa G Félix
- Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan, Mexico
| | - Rodolfo Ostos
- Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan, Mexico
| | - Armando J González
- Academic Unit of Computing, Master Program in Applied Sciences, Universidad Politecnica de Sinaloa, Mazatlan, Mexico
| | | | - Jesus D Melgarejo
- Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Gladys E Maestre
- Departments of Neurosciences and Human Genetics, and Rio Grande Valley Alzheimer´s Disease Resource Center for Minority Aging Research, University of Texas Rio Grande Valley, Brownsville, TX, United States
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Li P, Laleg-Kirati TM. Schrödinger Spectrum Based PPG Features for the Estimation of the Arterial Blood Pressure. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2683-2686. [PMID: 33018559 DOI: 10.1109/embc44109.2020.9176849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this paper, photoplethysmogram (PPG) features are combined with supervised machine learning algorithms to estimate arterial blood pressure (ABP). Three algorithms for the estimation of cuffless ABP using PPG signals are compared. Since PPG signals are measured non-invasively, this method guarantees an individuals comfort while not omitting important ABP information. The proposed framework predicts the ABP values by processing PPG signals with semi-classical signal analysis (SCSA) method, extracting several categories of features, which reflect the PPG signal morphology variations. Then, regression algorithms are selected for the ABP estimation. The proposed method is evaluated based on a virtual dataset with more than four thousand subjects and MIMIC II database with over eight thousand subjects for model training and testing. Mean average error (MAE) and standard deviation (STD) are evaluated for different machine learning algorithms during the prediction and estimation process. Multiple linear regression (MLR) meets the AAMI standard in terms of estimation accuracy, which proves that the ABP can be accurately estimated in a nonintrusive fashion. Given the easy implementation of the ABP estimation method, we regard that the proposed features and machine learning algorithms for the cuffless estimation of the ABP can potentially provide the means for mobile healthcare equipment to monitor the ABP continuously.
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Herbert R, Lim HR, Yeo WH. Printed, Soft, Nanostructured Strain Sensors for Monitoring of Structural Health and Human Physiology. ACS APPLIED MATERIALS & INTERFACES 2020; 12:25020-25030. [PMID: 32393022 DOI: 10.1021/acsami.0c04857] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Soft strain sensors that are mechanically flexible or stretchable are of significant interest in the fields of structural health monitoring, human physiology, and human-machine interfaces. However, existing deformable strain sensors still suffer from complex fabrication processes, poor reusability, limited adhesion strength, or structural rigidity. In this work, we introduce a versatile, high-throughput fabrication method of nanostructured, soft material-enabled, miniaturized strain sensors for both structural health monitoring and human physiology detection. Aerosol jet printing of polyimide and silver nanowires enables multifunctional strain sensors with tunable resistance and gauge factor. Experimental study of soft material compositions and multilayered structures of the strain sensor demonstrates the capabilities of strong adhesion and conformal lamination on different surfaces without the use of conventional fixtures and/or tapes. A two-axis, printed strain gauge enables the detection of force-induced strain changes on a curved stem valve for structural health management while offering reusability over 10 times without losing the sensing performance. Direct comparison with a commercial film sensor captures the advantages of the printed soft sensor in enhanced gauge factor and sensitivity. Another type of a stretchable strain sensor in skin-wearable applications demonstrates a highly sensitive monitoring of a subject's motion, pulse, and breathing, validated by comparing it with a clinical-grade system. Overall, the presented comprehensive study of materials, mechanics, printing-based fabrication, and interfacial adhesion shows a great potential of the printed soft strain sensor for applications in continuous structural health monitoring, human health detection, machine-interfacing systems, and environmental condition monitoring.
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Affiliation(s)
- Robert Herbert
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hyo-Ryoung Lim
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Parker H. Petit Institute for Bioengineering and Biosciences, Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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Voss A, Bogdanski M, Langohr B, Albrecht R, Sandbothe M. Mindfulness-Based Student Training Leads to a Reduction in Physiological Evaluated Stress. Front Psychol 2020; 11:645. [PMID: 32477199 PMCID: PMC7240125 DOI: 10.3389/fpsyg.2020.00645] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/18/2020] [Indexed: 12/13/2022] Open
Abstract
Background and Objective In today’s fast-paced modern lifestyle, chronic stress has become a serious issue with potential consequences for our physical and mental health. The concept of mindfulness and its derived Mindfulness-Based Stress Reduction (MBSR) program is considered to be an effective stress management technique for patients as well as for healthy persons. The effects of MBSR interventions on their participants have been subject of previous research, especially with regard to psychological or social science approaches using self-reports and questionnaires. In contrast, medical investigations in this field have been less frequent and often somehow limited, for example, addressing only absolute (discrete) mean values for heart rate or blood pressure. Methods In this study, we have evaluated a Mindfulness Based Student Training program (MBST) by applying methods of biosignal analysis to examine its impact on the training participants’ autonomic regulation. This intervention program included classical MBSR elements but was adapted to suit the normal daily needs of university students. We obtained the electrocardiogram, finger-pulse plethysmography, and respiration activity from students participating in either the intervention group (IGR, 38 subjects) or a passive control group (CON, 35 subjects) prior to and after 8 weeks of MBST intervention. Results When comparing various indices from heart rate variability, pulse wave variability, and respiration in linear and nonlinear domains, significant changes in the autonomic regulation were observed for the IGR group after 8 weeks of MBST. Conclusion The results indicate a reduced stress level exclusively for the intervention participants, and therefore, we assume a health benefit from the MBST program.
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Affiliation(s)
- Andreas Voss
- Institute of Innovative Health Technologies (IGHT), Ernst-Abbe-Hochschule Jena, Jena, Germany
| | - Martin Bogdanski
- Institute of Innovative Health Technologies (IGHT), Ernst-Abbe-Hochschule Jena, Jena, Germany
| | | | - Reyk Albrecht
- Faculty of Social and Behavioral Sciences, Friedrich-Schiller-University Jena, Jena, Germany
| | - Mike Sandbothe
- Institute of Innovative Health Technologies (IGHT), Ernst-Abbe-Hochschule Jena, Jena, Germany.,Department of Social Work, Ernst-Abbe-Hochschule Jena, Jena, Germany
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Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension. J Clin Med 2020; 9:jcm9041203. [PMID: 32331360 PMCID: PMC7230564 DOI: 10.3390/jcm9041203] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/07/2020] [Accepted: 04/13/2020] [Indexed: 12/14/2022] Open
Abstract
Elevated blood pressure (BP) is a major cause of death, yet hypertension commonly goes undetected. Owing to its nature, it is typically asymptomatic until later in its progression when the vessel or organ structure has already been compromised. Therefore, noninvasive and continuous BP measurement methods are needed to ensure appropriate diagnosis and early management before hypertension leads to irreversible complications. Photoplethysmography (PPG) is a noninvasive technology with waveform morphologies similar to that of arterial BP waveforms, therefore attracting interest regarding its usability in BP estimation. In recent years, wearable devices incorporating PPG sensors have been proposed to improve the early diagnosis and management of hypertension. Additionally, the need for improved accuracy and convenience has led to the development of devices that incorporate multiple different biosignals with PPG. Through the addition of modalities such as an electrocardiogram, a final measure of the pulse wave velocity is derived, which has been proved to be inversely correlated to BP and to yield accurate estimations. This paper reviews and summarizes recent studies within the period 2010–2019 that combined PPG with other biosignals and offers perspectives on the strengths and weaknesses of current developments to guide future advancements in BP measurement. Our literature review reveals promising measurement accuracies and we comment on the effective combinations of modalities and success of this technology.
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Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices. JOURNAL OF HEALTHCARE ENGINEERING 2019; 2019:3924508. [PMID: 31316740 PMCID: PMC6604299 DOI: 10.1155/2019/3924508] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/22/2019] [Accepted: 05/29/2019] [Indexed: 12/03/2022]
Abstract
The healthcare-related functions of wearable devices are very useful for continuous monitoring of biological information. Wearable devices equipped with communication function can be used for additional healthcare services. Among the wearable devices, the wristband type is most suitable for acquiring biological signals, and the wear preference of the user is high, so it is highly likely to be used more in the future. In this paper, the health-related functions of wristband were investigated and the technical limitations and prospects were also reviewed. Most current wristband-type devices are equipped with the combination of accelerometer, optical sensor, and electrodes for their health functions, and continuously measured data are expanding the possibility of discovering new medical meanings. The blood pressure measurement function without using cuff is the most useful and expected function among the health-related functions expected to be mounted on the wrist wearable device, in spite of its technical limits and difficulties.
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An Unobtrusive and Calibration-free Blood Pressure Estimation Method using Photoplethysmography and Biometrics. Sci Rep 2019; 9:8611. [PMID: 31197243 PMCID: PMC6565722 DOI: 10.1038/s41598-019-45175-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/23/2019] [Indexed: 01/01/2023] Open
Abstract
We introduce a novel paradigm to unobtrusively and optically measure blood pressure (BP) without calibration. The algorithm combines photoplethysmography (PPG) waveform analysis and biometrics to estimate BP, and was evaluated in subjects with various age, height, weight and BP levels (n = 1249). In the young population (<50 years old) with low, medium and high systolic blood pressures (SBP, <120 mmHg; 120–139 mmHg; ≥140 mmHg), the fitting errors are 6.3 ± 7.2, −3.9 ± 7.2 and −20.2 ± 14.2 mmHg for SBP respectively; In the older population (>50 years old) with the same categories, the fitting errors are 12.8 ± 9.0, 0.5 ± 8.2 and −14.6 ± 11.5 mmHg for SBP respectively. A simple personalized calibration reduces fitting errors significantly (n = 147), and good peripheral perfusion helps to improve the fitting accuracy. In conclusion, PPG may be used to calculate BP without calibration in certain populations. When calibrated, it shows great potential to serially monitor BP fluctuation, which can bring tremendous economic and health benefits.
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Kaisti M, Panula T, Leppänen J, Punkkinen R, Jafari Tadi M, Vasankari T, Jaakkola S, Kiviniemi T, Airaksinen J, Kostiainen P, Meriheinä U, Koivisto T, Pänkäälä M. Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation. NPJ Digit Med 2019; 2:39. [PMID: 31304385 PMCID: PMC6550190 DOI: 10.1038/s41746-019-0117-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Accepted: 04/11/2019] [Indexed: 01/07/2023] Open
Abstract
There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording (n = 18), (ii) analyzing the ability to detect irregularities of the rhythm (n = 7), and (iii) measuring the heartrate monitoring accuracy (n = 31). Arterial waveforms carry important physiological information and the comparison study revealed that the recordings made with the wearable device and with the gold standard device resulted in almost identical (r = 0.9–0.99) pulse waveforms. The device can measure the heart rhythm and possible irregularities in it. A clustering analysis demonstrates a perfect classification accuracy between atrial fibrillation (AF) and sinus rhythm. The heartrate monitoring study showed near perfect beat-to-beat accuracy (sensitivity = 99.1%, precision = 100%) on healthy subjects. In contrast, beat-to-beat detection from coronary artery disease patients was challenging, but the averaged heartrate was extracted successfully (95% CI: −1.2 to 1.1 bpm). In conclusion, the results indicate that the device could be useful in remote monitoring of cardiovascular diseases and personalized medicine.
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Affiliation(s)
- Matti Kaisti
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland.,2Department of Bioengineering, Imperial College London, London, SW7 2AZ UK
| | - Tuukka Panula
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland
| | | | - Risto Punkkinen
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland
| | - Mojtaba Jafari Tadi
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland
| | - Tuija Vasankari
- 4Heart Center, Turku University Hospital and University of Turku, 20521 Turku, Finland
| | - Samuli Jaakkola
- 4Heart Center, Turku University Hospital and University of Turku, 20521 Turku, Finland
| | - Tuomas Kiviniemi
- 4Heart Center, Turku University Hospital and University of Turku, 20521 Turku, Finland.,5Harvard Medical School, MacRae Laboratory Brigham and Women's Hospital, Boston, MA 02115 USA
| | - Juhani Airaksinen
- 4Heart Center, Turku University Hospital and University of Turku, 20521 Turku, Finland
| | | | | | - Tero Koivisto
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland
| | - Mikko Pänkäälä
- 1Department of Future Technologies, University of Turku, 20500 Turku, Finland
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Liang Y, Abbott D, Howard N, Lim K, Ward R, Elgendi M. How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database. J Clin Med 2019; 8:E337. [PMID: 30862031 PMCID: PMC6462898 DOI: 10.3390/jcm8030337] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 03/05/2019] [Accepted: 03/06/2019] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease (CVD) is the number one cause of non-infectious morbidity and mortality in the world. The detection, measurement, and management of high blood pressure play an essential role in the prevention and control of CVDs. However, owing to the limitations and discomfort of traditional blood pressure (BP) detection techniques, many new cuff-less blood pressure approaches have been proposed and explored. Most of these involve arterial wave propagation theory, which is based on pulse arrival time (PAT), the time interval needed for a pulse wave to travel from the heart to some distal place on the body, such as the finger or earlobe. For this study, the Medical Information Mart for Intensive Care (MIMIC) database was used as a benchmark for PAT analysis. Many researchers who use the MIMIC database make the erroneous assumption that all the signals are synchronized. Therefore, we decided to investigate the calculation of PAT intervals in the MIMIC database and check its usefulness for evaluating BP. Our findings have important implications for the future use of the MIMIC database, especially for BP evaluation.
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Affiliation(s)
- Yongbo Liang
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada.
| | - Derek Abbott
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide SA 5005, Australia.
- Centre for Biomedical Engineering, The University of Adelaide, Adelaide SA 5005, Australia.
| | - Newton Howard
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK.
- Howard Brain Sciences Foundation, Providence, RI 02906, USA.
| | - Kenneth Lim
- Faculty of Medicine, University of British Columbia, Vancouver BC V1Y 1T3, Canada.
- BC Children's & Women's Hospital, Vancouver BC V6H 3N1, Canada.
| | - Rabab Ward
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada.
| | - Mohamed Elgendi
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver BC V6T 1Z4, Canada.
- Howard Brain Sciences Foundation, Providence, RI 02906, USA.
- Faculty of Medicine, University of British Columbia, Vancouver BC V1Y 1T3, Canada.
- BC Children's & Women's Hospital, Vancouver BC V6H 3N1, Canada.
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Hypertension Assessment Using Photoplethysmography: A Risk Stratification Approach. J Clin Med 2018; 8:jcm8010012. [PMID: 30577637 PMCID: PMC6352119 DOI: 10.3390/jcm8010012] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 11/29/2018] [Accepted: 12/14/2018] [Indexed: 12/01/2022] Open
Abstract
Hypertension is a common chronic cardiovascular disease (CVD). Early screening and diagnosis of hypertension plays a major role in its prevention and in the control of CVDs. Our study discusses the early screening of hypertension while using the morphological features of photoplethysmography (PPG). Numerous morphological features of PPG and its derivative waves were defined and extracted. Six types of feature selection methods were chosen to screen and evaluate these PPG morphological features. The optimal features were comprehensively analyzed in relation to the physiological processes of the cardiovascular circulatory system. Particularly, the intrinsic relation and physiological significance between the formation process of systolic blood pressure (SBP) and PPG morphology features were analyzed in depth. A variety of linear and nonlinear classification models were established for the comparison trials. The F1 scores for the normotension versus prehypertension, normotension and prehypertension versus hypertension, and normotension versus hypertension trials were 72.97%, 81.82%, and 92.31%, respectively. In summary, this study established a PPG characteristic analysis model and established the intrinsic relationship between SBP and PPG characteristics. Finally, the risk stratification of hypertension at different stages was examined and compared based on the optimal feature subset.
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