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Henry B, Merz M, Hoang H, Abdulkarim G, Wosik J, Schoettker P. Cuffless Blood Pressure in clinical practice: challenges, opportunities and current limits. Blood Press 2024; 33:2304190. [PMID: 38245864 DOI: 10.1080/08037051.2024.2304190] [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: 11/01/2023] [Accepted: 01/07/2024] [Indexed: 01/22/2024]
Abstract
Background: Cuffless blood pressure measurement technologies have attracted significant attention for their potential to transform cardiovascular monitoring.Methods: This updated narrative review thoroughly examines the challenges, opportunities, and limitations associated with the implementation of cuffless blood pressure monitoring systems.Results: Diverse technologies, including photoplethysmography, tonometry, and ECG analysis, enable cuffless blood pressure measurement and are integrated into devices like smartphones and smartwatches. Signal processing emerges as a critical aspect, dictating the accuracy and reliability of readings. Despite its potential, the integration of cuffless technologies into clinical practice faces obstacles, including the need to address concerns related to accuracy, calibration, and standardization across diverse devices and patient populations. The development of robust algorithms to mitigate artifacts and environmental disturbances is essential for extracting clear physiological signals. Based on extensive research, this review emphasizes the necessity for standardized protocols, validation studies, and regulatory frameworks to ensure the reliability and safety of cuffless blood pressure monitoring devices and their implementation in mainstream medical practice. Interdisciplinary collaborations between engineers, clinicians, and regulatory bodies are crucial to address technical, clinical, and regulatory complexities during implementation. In conclusion, while cuffless blood pressure monitoring holds immense potential to transform cardiovascular care. The resolution of existing challenges and the establishment of rigorous standards are imperative for its seamless incorporation into routine clinical practice.Conclusion: The emergence of these new technologies shifts the paradigm of cardiovascular health management, presenting a new possibility for non-invasive continuous and dynamic monitoring. The concept of cuffless blood pressure measurement is viable and more finely tuned devices are expected to enter the market, which could redefine our understanding of blood pressure and hypertension.
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Affiliation(s)
- Benoit Henry
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maxime Merz
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Harry Hoang
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ghaith Abdulkarim
- Neuro-Informatics Laboratory, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
- Department of Neurological Surgery, Mayo Clinic, Rochester, MN, USA
| | - Jedrek Wosik
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Patrick Schoettker
- Service of Anesthesiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Bantwal AS, Bhayadia AK, Meng H. Critical role of arterial constitutive model in predicting blood pressure from pulse wave velocity. Comput Biol Med 2024; 178:108730. [PMID: 38917535 DOI: 10.1016/j.compbiomed.2024.108730] [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: 04/19/2024] [Revised: 05/29/2024] [Accepted: 06/08/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND A promising approach to cuff-less, continuous blood pressure monitoring is to estimate blood pressure (BP) from Pulse Wave Velocity (PWV). However, most existing PWV-based methods rely on empirical BP-PWV relations and have large prediction errors, which may be caused by the implicit assumption of thin-walled, linear elastic arteries undergoing small deformations. Our objective is to understand the BP-PWV relationship in the absence of such limiting assumptions. METHOD We performed Fluid-Structure Interaction (FSI) simulations of the radial artery and the common carotid artery under physiological flow conditions. In these dynamic simulations, we employed two constitutive models for the arterial wall: the linear elastic model, implying a thin-walled linear elastic artery undergoing small deformations, and the Holzapfel-Gasser-Ogden (HGO) model, accounting for the nonlinear effects of collagen fibers and their orientations on the large arterial deformation. RESULTS Despite the changing BP, the linear elastic model predicts a constant PWV throughout a cardiac cycle, which is not physiological. The HGO model correctly predicts a positive BP-PWV correlation by capturing the nonlinear deformation of the artery, showing up to 50 % variations of PWV in a cardiac cycle. CONCLUSION Dynamic FSI simulations reveal that the BP-PWV relationship strongly depends on the arterial constitutive model, especially in the radial artery. To infer BP from PWV, one must account for the varying PWV, a consequence of the nonlinear arterial response due to collagen fibers. Future efforts should be directed towards robust measurement of time-varying PWV if it is to be used to predict BP.
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Affiliation(s)
| | - Amit Kumar Bhayadia
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
| | - Hui Meng
- Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, 14260, USA.
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Wu BF, Chiu LW, Wu YC, Lai CC, Cheng HM, Chu PH. Contactless Blood Pressure Measurement Via Remote Photoplethysmography With Synthetic Data Generation Using Generative Adversarial Networks. IEEE J Biomed Health Inform 2024; 28:621-632. [PMID: 37037253 DOI: 10.1109/jbhi.2023.3265857] [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: 04/12/2023]
Abstract
Remote photoplethysmography (rPPG) has been used to measure vital signs such as heart rate, heart rate variability, blood pressure (BP), and blood oxygen. Recent studies adopt features developed with photoplethysmography (PPG) to achieve contactless BP measurement via rPPG. These features can be classified into two groups: time or phase differences from multiple signals, or waveform feature analysis from a single signal. Here we devise a solution to extract the time difference information from the rPPG signal captured at 30 FPS. We also propose a deep learning model architecture to estimate BP from the extracted features. To prevent overfitting and compensate for the lack of data, we leverage a multi-model design and generate synthetic data. We also use subject information related to BP to assist in model learning. For real-world usage, the subject information is replaced with values estimated from face images, with performance that is still better than the state-of-the-art. To our best knowledge, the improvements can be achieved because of: 1) the model selection with estimated subject information, 2) replacing the estimated subject information with the real one, 3) the InfoGAN assistance training (synthetic data generation), and 4) the time difference features as model input. To evaluate the performance of the proposed method, we conduct a series of experiments, including dynamic BP measurement for many single subjects and nighttime BP measurement with infrared lighting. Our approach reduces the MAE from 15.49 to 8.78 mmHg for systolic blood pressure (SBP) and 10.56 to 6.16 mmHg for diastolic blood pressure(DBP) on a self-constructed rPPG dataset. On the Taipei Veterans General Hospital(TVGH) dataset for nighttime applications, the MAE is reduced from 21.58 to 11.12 mmHg for SBP and 9.74 to 7.59 mmHg for DBP, with improvement ratios of 48.47% and 22.07% respectively.
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Wang L, Tian S, Zhu R. A new method of continuous blood pressure monitoring using multichannel sensing signals on the wrist. MICROSYSTEMS & NANOENGINEERING 2023; 9:117. [PMID: 37744263 PMCID: PMC10511443 DOI: 10.1038/s41378-023-00590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/06/2023] [Accepted: 07/31/2023] [Indexed: 09/26/2023]
Abstract
Hypertension is a worldwide health problem and a primary risk factor for cardiovascular disease. Continuous monitoring of blood pressure has important clinical value for the early diagnosis and prevention of cardiovascular disease. However, existing technologies for wearable continuous blood pressure monitoring are usually inaccurate, rely on subject-specific calibration and have poor generalization across individuals, which limit their practical applications. Here, we report a new blood pressure measurement method and develop an associated wearable device to implement continuous blood pressure monitoring for new subjects. The wearable device detects cardiac output and pulse waveform features through dual photoplethysmography (PPG) sensors worn on the palmar and dorsal sides of the wrist, incorporating custom-made interface sensors to detect the wearing contact pressure and skin temperature. The detected multichannel signals are fused using a machine-learning algorithm to estimate continuous blood pressure in real time. This dual PPG sensing method effectively eliminates the personal differences in PPG signals caused by different people and different wearing conditions. The proposed wearable device enables continuous blood pressure monitoring with good generalizability across individuals and demonstrates promising potential in personal health care applications.
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Affiliation(s)
- Liangqi Wang
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Shuo Tian
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
| | - Rong Zhu
- State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
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5
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Martinez J, Passage B, Mortazavi BJ, Jafari R. Hypothesis Scoring for Confidence-Aware Blood Pressure Estimation With Particle Filters. IEEE J Biomed Health Inform 2023; 27:4273-4284. [PMID: 37363851 PMCID: PMC10567135 DOI: 10.1109/jbhi.2023.3289192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2023]
Abstract
We propose our Confidence-Aware Particle Filter (CAPF) framework that analyzes a series of estimated changes in blood pressure (BP) to provide several true state hypotheses for a given instance. Particularly, our novel confidence-awareness mechanism assigns likelihood scores to each hypothesis in an effort to discard potentially erroneous measurements - based on the agreement amongst a series of estimated changes and the physiological plausibility when considering DBP/SBP pairs. The particle filter formulation (or sequential Monte Carlo method) can jointly consider the hypotheses and their probabilities over time to provide a stable trend of estimated BP measurements. In this study, we evaluate BP trend estimation from an emerging bio-impedance (Bio-Z) prototype wearable modality although it is applicable to all types of physiological modalities. Each subject in the evaluation cohort underwent a hand-gripper exercise, a cold pressor test, and a recovery state to increase the variation to the captured BP ranges. Experiments show that CAPF yields superior continuous pulse pressure (PP), diastolic blood pressure (DBP), and systolic blood pressure (SBP) estimation performance compared to ten baseline approaches. Furthermore, CAPF performs on track to comply with AAMI and BHS standards for achieving a performance classification of Grade A, with mean error accuracies of -0.16 ± 3.75 mmHg for PP (r = 0.81), 0.42 ± 4.39 mmHg for DBP (r = 0.92), and -0.09 ± 6.51 mmHg for SBP (r = 0.92) from more than test 3500 data points.
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Huang Y, Huang D, Huang J, Lu H, He M, Wang W. Camera Wavelength Selection for Multi-wavelength Pulse Transit Time based Blood Pressure Monitoring. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083039 DOI: 10.1109/embc40787.2023.10340068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Multi-wavelength pulse transmit time (MV-PTT) is a potential tool for remote blood pressure (BP) monitoring. It uses two wavelengths, typically green (G) and near-infrared (NIR), that have different skin penetration depths to measure the PTT between artery and arterioles of a single site of the skin for BP estimation. However, the impact of wavelength selection for MV-PTT based BP calibration is unknown. In this paper, we explore the combination of different wavelengths of camera photoplethysmography for BP measurement using a modified narrow-band camera centered at G-550/R-660/NIR-850 nm, especially focused on the comparison between G-R (full visible) and G-NIR (hybrid). The experiment was conducted on 17 adult participants in a dark chamber with their BP significantly changed by the protocol of ice water stimulation. The experimental results show that the MV-PTT obtained by G-NIR has a higher correlation with BP, and the fitted model has lower MAE in both the systolic pressure (5.78 mmHg) and diastolic pressure (6.67 mmHg) than others. It is confirmed that a hybrid wavelength of visible (G) and NIR is still essential for accurate BP calibration due to their difference in skin penetration depth that allows proper sensing of different skin layers for this measurement.
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7
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Sel K, Mohammadi A, Pettigrew RI, Jafari R. Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation. NPJ Digit Med 2023; 6:110. [PMID: 37296218 DOI: 10.1038/s41746-023-00853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Amirmohammad Mohammadi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA.
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8
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Zhao L, Liang C, Huang Y, Zhou G, Xiao Y, Ji N, Zhang YT, Zhao N. Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring. NPJ Digit Med 2023; 6:93. [PMID: 37217650 DOI: 10.1038/s41746-023-00835-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.
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Affiliation(s)
- Lei Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Cunman Liang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yan Huang
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Guodong Zhou
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yiqun Xiao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Nan Ji
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Yuan-Ting Zhang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Hong Kong Center for Cerebro-Cardiovascular Health Engineering (COCHE), Hong Kong, China.
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9
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Seo Y, Kwon S, Sunarya U, Park S, Park K, Jung D, Cho Y, Park C. Blood pressure estimation and its recalibration assessment using wrist cuff blood pressure monitor. Biomed Eng Lett 2023; 13:221-233. [PMID: 37124108 PMCID: PMC10130301 DOI: 10.1007/s13534-023-00271-1] [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: 10/20/2022] [Revised: 01/02/2023] [Accepted: 02/16/2023] [Indexed: 05/02/2023] Open
Abstract
The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.
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Affiliation(s)
- Youjung Seo
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
| | - Saehim Kwon
- Department of Artificial Intelligence, Kwangwoon University, Seoul, 01897 Korea
| | - Unang Sunarya
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
- School of Applied Science, Telkom University, Bandung, 40257 Indonesia
| | - Sungmin Park
- Department of Convergence IT Engineering and the Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673 Korea
| | - Kwangsuk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, 03080 Korea
| | - Dawoon Jung
- Center for Artificial Intelligence, Korea Institute of Science and Technology, Seoul, 13916 Korea
| | - Youngho Cho
- Department of Electrical and Communication Engineering, University of Daelim, Anyang, 13916 Korea
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul, 01897 Korea
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10
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Vysotskaya N, Will C, Servadei L, Maul N, Mandl C, Nau M, Harnisch J, Maier A. Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:4111. [PMID: 37112454 PMCID: PMC10145629 DOI: 10.3390/s23084111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 06/19/2023]
Abstract
Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.
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Affiliation(s)
- Nastassia Vysotskaya
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
- Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany
| | - Christoph Will
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
| | - Lorenzo Servadei
- Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
| | - Noah Maul
- Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany
| | - Christian Mandl
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
| | - Merlin Nau
- Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany
| | - Jens Harnisch
- Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany
| | - Andreas Maier
- Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany
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Toda S, Matsumura K. Investigation of Optimal Light Source Wavelength for Cuffless Blood Pressure Estimation Using a Single Photoplethysmography Sensor. SENSORS (BASEL, SWITZERLAND) 2023; 23:3689. [PMID: 37050747 PMCID: PMC10098792 DOI: 10.3390/s23073689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/30/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Routine blood pressure measurement is important for the early detection of various diseases. Recently, cuffless blood pressure estimation methods that do not require cuff pressurization have attracted attention. In this study, we investigated the effect of the light source wavelength on the accuracy of blood pressure estimation using only two physiological indices that can be calculated with photoplethysmography alone, namely, heart rate and modified normalized pulse volume. Using a newly developed photoplethysmography sensor that can simultaneously measure photoplethysmograms at four wavelengths, we evaluated its estimation accuracy for systolic blood pressure, diastolic blood pressure, and mean arterial pressure against a standard cuff sphygmomanometer. Mental stress tasks were used to alter the blood pressure of 14 participants, and multiple linear regression analysis showed the best light sources to be near-infrared for systolic blood pressure and blue for both diastolic blood pressure and mean arterial pressure. The importance of the light source wavelength for the photoplethysmogram in cuffless blood pressure estimation was clarified.
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Affiliation(s)
- Sogo Toda
- Ishikawa College, National Institute of Technology, Tsubata 929-0392, Japan
| | - Kenta Matsumura
- Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
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12
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Chen Y, Zhuang J, Li B, Zhang Y, Zheng X. Remote Blood Pressure Estimation via the Spatiotemporal Mapping of Facial Videos. SENSORS (BASEL, SWITZERLAND) 2023; 23:2963. [PMID: 36991677 PMCID: PMC10055237 DOI: 10.3390/s23062963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.
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Affiliation(s)
- Yuheng Chen
- Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, China
- Key Laboratory of Information and Automation Technology of Sichuan Province, Chengdu 610065, China
| | - Jialiang Zhuang
- Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, China
| | - Bin Li
- School of Computer Science, Northwest University, Xi’an 710069, China
| | - Yun Zhang
- School of Information Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
| | - Xiujuan Zheng
- Department of Automation, College of Electrical Engineering, Sichuan University, Chengdu 610065, China
- Key Laboratory of Information and Automation Technology of Sichuan Province, Chengdu 610065, China
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Zhou ZB, Cui TR, Li D, Jian JM, Li Z, Ji SR, Li X, Xu JD, Liu HF, Yang Y, Ren TL. Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review. MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16062133. [PMID: 36984013 PMCID: PMC10057755 DOI: 10.3390/ma16062133] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 06/12/2023]
Abstract
Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.
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Affiliation(s)
- Zi-Bo Zhou
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
| | - Tian-Rui Cui
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Ding Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jin-Ming Jian
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Zhen Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Shou-Rui Ji
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Xin Li
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Jian-Dong Xu
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Hou-Fang Liu
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Yi Yang
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
| | - Tian-Ling Ren
- School of Integrated Circuit, Tsinghua University, Beijing 100084, China
- Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
- Center for Flexible Electronics Technology, Tsinghua University, Beijing 100084, China
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14
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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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15
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Hammoud A, Tikhomirov A, Briko A, Volkov A, Karapetyan A, Shchukin S. Evaluation of the Information Content for Determining the Vascular Tone Type of the Lower Extremities in Varicose Veins: A Case Study. BIOSENSORS 2023; 13:96. [PMID: 36671931 PMCID: PMC9855907 DOI: 10.3390/bios13010096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
The incidence of cardiovascular diseases is continuously increasing around the world. Therefore, the study of new methods for diagnosing cardiovascular diseases is very important. Early diagnosis and evaluation of the effectiveness of treatments are among the most important tasks. In this work, we study changes in vascular compliance and vascular tone of the lower extremities in a patient diagnosed with an early stage of varicose veins. The study is based on recording the bioimpedance signals of the lower extremities and their parts using the Rheo-32 multichannel device. Registration in the monitoring system takes place in two stages: the first in a state of relaxation, and the second after applying a local massage on one of the legs for five minutes. The results indicate a change in the type of vascular tone of the lower extremities after the massage, while the type of vascular tone changes and shifts on average towards the normotonic type. The method proposed in this study makes it possible to quantitatively and qualitatively assess changes in the tone of the vessels of the extremities.
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Affiliation(s)
- Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
| | - Alexey Tikhomirov
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
| | - Andrey Briko
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
| | - Alexander Volkov
- Scientific and Educational Medical-Technological Center, Bauman Moscow State Technical University, 105005 Moscow, Russia
| | - Aida Karapetyan
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
| | - Sergey Shchukin
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia
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16
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Man PK, Cheung KL, Sangsiri N, Shek WJ, Wong KL, Chin JW, Chan TT, So RHY. Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring. Healthcare (Basel) 2022; 10:healthcare10102113. [PMID: 36292560 PMCID: PMC9601911 DOI: 10.3390/healthcare10102113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/26/2022] [Accepted: 10/02/2022] [Indexed: 11/04/2022] Open
Abstract
Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.
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Affiliation(s)
- Ping-Kwan Man
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Correspondence:
| | - Kit-Leong Cheung
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Nawapon Sangsiri
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Wilfred Jin Shek
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Biomedical Sciences, King’s College London, London WC2R 2LS, UK
| | - Kwan-Long Wong
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Jing-Wei Chin
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Tsz-Tai Chan
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Richard Hau-Yue So
- PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
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17
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Galli A, Montree RJH, Que S, Peri E, Vullings R. An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22114035. [PMID: 35684656 PMCID: PMC9185322 DOI: 10.3390/s22114035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 06/02/2023]
Abstract
This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.
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Affiliation(s)
- Alessandra Galli
- Department of Information Engineering, University of Padova, I-35131 Padova, Italy;
| | - Roel J. H. Montree
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Shuhao Que
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Elisabetta Peri
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
| | - Rik Vullings
- Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands; (R.J.H.M.); (S.Q.); (E.P.)
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18
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Athaya T, Choi S. A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22103953. [PMID: 35632360 PMCID: PMC9145242 DOI: 10.3390/s22103953] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/19/2022] [Accepted: 05/21/2022] [Indexed: 05/06/2023]
Abstract
Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient's health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.
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19
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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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