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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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Quattrocchi A, Garufi G, Gugliandolo G, De Marchis C, Collufio D, Cardali SM, Donato N. Handgrip Strength in Health Applications: A Review of the Measurement Methodologies and Influencing Factors. SENSORS (BASEL, SWITZERLAND) 2024; 24:5100. [PMID: 39204796 PMCID: PMC11359434 DOI: 10.3390/s24165100] [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: 06/24/2024] [Revised: 07/23/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
This narrative review provides a comprehensive analysis of the several methods and technologies employed to measure handgrip strength (HGS), a significant indicator of neuromuscular strength and overall health. The document evaluates a range of devices, from traditional dynamometers to innovative sensor-based systems, and assesses their effectiveness and application in different demographic groups. Special attention is given to the methodological aspects of HGS estimation, including the influence of device design and measurement protocols. Endogenous factors such as hand dominance and size, body mass, age and gender, as well as exogenous factors including circadian influences and psychological factors, are examined. The review identifies significant variations in the implementation of HGS measurements and interpretation of the resultant data, emphasizing the need for careful consideration of these factors when using HGS as a diagnostic or research tool. It highlights the necessity of standardizing measurement protocols to establish universal guidelines that enhance the comparability and consistency of HGS assessments across various settings and populations.
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Affiliation(s)
- Antonino Quattrocchi
- Department of Engineering, University of Messina, 98166 Messina, Italy; (G.G.); (N.D.)
| | - Giada Garufi
- Department of Neurosurgery, Azienda Ospedaliera Papardo, University of Messina, 98158 Messina, Italy; (G.G.); (D.C.); (S.M.C.)
| | - Giovanni Gugliandolo
- Department of Engineering, University of Messina, 98166 Messina, Italy; (G.G.); (N.D.)
| | - Cristiano De Marchis
- Department of Engineering, University of Messina, 98166 Messina, Italy; (G.G.); (N.D.)
| | - Domenicantonio Collufio
- Department of Neurosurgery, Azienda Ospedaliera Papardo, University of Messina, 98158 Messina, Italy; (G.G.); (D.C.); (S.M.C.)
| | - Salvatore Massimiliano Cardali
- Department of Neurosurgery, Azienda Ospedaliera Papardo, University of Messina, 98158 Messina, Italy; (G.G.); (D.C.); (S.M.C.)
- Division of Neurosurgery, BIOMORF Department, University of Messina, 98124 Messina, Italy
| | - Nicola Donato
- Department of Engineering, University of Messina, 98166 Messina, Italy; (G.G.); (N.D.)
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Pittella E, Testa O, Podestà L, Piuzzi E. An Optical Signal Simulator for the Characterization of Photoplethysmographic Devices. SENSORS (BASEL, SWITZERLAND) 2024; 24:1008. [PMID: 38339729 PMCID: PMC10857427 DOI: 10.3390/s24031008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/25/2024] [Accepted: 01/31/2024] [Indexed: 02/12/2024]
Abstract
(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.
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Affiliation(s)
- Erika Pittella
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
| | - Orlandino Testa
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
| | - Luca Podestà
- Department of Astronautical, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy;
| | - Emanuele Piuzzi
- Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, 00184 Rome, Italy; (O.T.); (E.P.)
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4
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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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5
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Liu Z, Zhang Y, Zhou C. BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG. Comput Biol Med 2024; 168:107795. [PMID: 38056206 DOI: 10.1016/j.compbiomed.2023.107795] [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: 09/19/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Physiological parameter monitoring based on photoplethysmography (PPG) detection has the advantage of fast, portable, and non-invasive. Changes in the morphology of the PPG waveform can reflect the effect of arterial elasticity changes on blood pressure (BP). However, machine learning models and non-recurrent neural network models typically ignore the time-dependency of continuous PPG data, leading to the decrease of accuracy or the increased calibration frequency. OBJECTIVE This paper proposes a BiGRU model with attention to estimate BP trends, which uses a single-channel PPG signal combined with demographic information to estimate continuous BP trends point-by-point and to discuss the impact of calibration cycle. METHODS This paper selects 15 typical subjects from two groups with/without cardiovascular disease (CVD) and evaluates the model performance. Regarding the calibration frequency problem, we set two modes of non-calibration and calibration to validate the results of blood pressure trends estimation. RESULTS In the calibration mode, the estimation errors (ME ± STD) of SBP for CVD/non-CVD groups are 0.91 ± 10.58 mmHg/0.17 ± 10.06 mmHg respectively, and DBP are 0.34 ± 5.28 mmHg/-0.19 ± 5.36 mmHg; in the non-calibration mode, the estimation errors of SBP for CVD/non-CVD groups are 0.27 ± 9.87 mmHg/-0.82 ± 9.92 mmHg respectively, and DBP are -0.63 ± 3.28 mmHg/0.80 ± 4.93 mmHg. CONCLUSIONS The results show that the proposed model has high accuracy in estimating BP levels, which is expected to achieve real-time, long-term continuous BP trends monitoring in wearable devices.
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Affiliation(s)
- Ziyi Liu
- Guangdong Transtek Medical Electronics Co., Ltd., Zhongshan, 52843, People's Republic of China
| | - Yiming Zhang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Congcong Zhou
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China; Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, National Engineering Research Center for Innovation and Application of Minimally Invasive Devices, 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, People's Republic of China.
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6
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Sarkar S, Ghosh A. Schrödinger spectrum based continuous cuff-less blood pressure estimation using clinically relevant features from PPG signal and its second derivative. Comput Biol Med 2023; 166:107558. [PMID: 37806054 DOI: 10.1016/j.compbiomed.2023.107558] [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: 04/06/2023] [Revised: 09/02/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
The presented study estimates cuff-less blood pressure (BP) from photoplethysmography (PPG) signals using multiple machine-learning (ML) models and the semi-classical signal analysis (SCSA) technique. The study proposes a novel signal reconstruction algorithm, which optimizes the semi-classical constant of the SCSA approach and eliminates the trade-off between complexity and accuracy during signal reconstruction. It predicts BP values using regression algorithms by processing reconstructed PPG signals' spectral features, extracting clinically relevant PPG and its second derivative's (SDPPG) morphological features. The developed method was assessed using a virtual in-silico dataset with more than 4000 subjects and the Multi-Parameter Intelligent Monitoring in Intensive Care Units (MIMIC-II) dataset. Results showed that the method attained a mean absolute error (MAE) of 5.37 and 2.96 mmHg for systolic and diastolic BP, respectively, using the CatBoost algorithm. This approach met the Association for the Advancement of Medical Instrumentation's standard and achieved Grade A for all BP categories in the British Hypertension Society protocol. The proposed framework performs well even when applied to a combined clinically relevant database originating from MIMIC-III and the Queensland dataset. The proposed method's performance is further evaluated in a non-clinical setting with noisy and deformed PPG signals to validate the efficacy of the SCSA method. The noise stress tests further showed that the algorithm maintained its key feature detection, signal reconstruction capability, and estimation accuracy up to a 10 dB SNR ratio. The proposed cuff-less BP estimation technique has the potential to perform well in mobile healthcare devices due to its straightforward implementation approach.
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Affiliation(s)
- Sayan Sarkar
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Aayushman Ghosh
- Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 11103, India; Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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7
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Liu ZD, Li Y, Zhang YT, Zeng J, Chen ZX, Cui ZW, Liu JK, Miao F. Cuffless Blood Pressure Measurement Using Smartwatches: A Large-Scale Validation Study. IEEE J Biomed Health Inform 2023; 27:4216-4227. [PMID: 37204948 DOI: 10.1109/jbhi.2023.3278168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This study aimed to evaluate the performance of cuffless blood pressure (BP) measurement techniques in a large and diverse cohort of participants. We enrolled 3077 participants (aged 18-75, 65.16% women, 35.91% hypertensive participants) and conducted followed-up for approximately 1 month. Electrocardiogram, pulse pressure wave, and multiwavelength photoplethysmogram signals were simultaneously recorded using smartwatches; dual-observer auscultation systolic BP (SBP) and diastolic BP (DBP) reference measurements were also obtained. Pulse transit time, traditional machine learning (TML), and deep learning (DL) models were evaluated with calibration and calibration-free strategy. TML models were developed using ridge regression, support vector machine, adaptive boosting, and random forest; while DL models using convolutional and recurrent neural networks. The best-performing calibration-based model yielded estimation errors of 1.33 ± 6.43 mmHg for DBP and 2.31 ± 9.57 mmHg for SBP in the overall population, with reduced SBP estimation errors in normotensive (1.97 ± 7.85 mmHg) and young (0.24 ± 6.61 mmHg) subpopulations. The best-performing calibration-free model had estimation errors of -0.29 ± 8.78 mmHg for DBP and -0.71 ± 13.04 mmHg for SBP. We conclude that smartwatches are effective for measuring DBP for all participants and SBP for normotensive and younger participants with calibration; performance degrades significantly for heterogeneous populations including older and hypertensive participants. The availability of cuffless BP measurement without calibration is limited in routine settings. Our study provides a large-scale benchmark for emerging investigations on cuffless BP measurement, highlighting the need to explore additional signals or principles to enhance the accuracy in large-scale heterogeneous populations.
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8
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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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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: 8] [Impact Index Per Article: 8.0] [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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Nour M, Kandaz D, Ucar MK, Polat K, Alhudhaif A. Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5714454. [PMID: 35903432 PMCID: PMC9325348 DOI: 10.1155/2022/5714454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/21/2022] [Accepted: 06/28/2022] [Indexed: 11/18/2022]
Abstract
Objective Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology. The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman's feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems. Results The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively. Conclusion According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.
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Affiliation(s)
- Majid Nour
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Derya Kandaz
- Electrical-Electronics Engineering, Faculty of Engineering, Sakarya University, 54187 Sakarya, Turkey
| | - Muhammed Kursad Ucar
- Electrical-Electronics Engineering, Faculty of Engineering, Sakarya University, 54187 Sakarya, Turkey
| | - Kemal Polat
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey
| | - Adi Alhudhaif
- Department of Computer Science, College of Computer Engineering and Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia
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11
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Landry C, Peterson SD, Arami A. A fusion approach to improve accuracy and estimate uncertainty in cuffless blood pressure monitoring. Sci Rep 2022; 12:7948. [PMID: 35562410 PMCID: PMC9106676 DOI: 10.1038/s41598-022-12087-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/29/2022] [Indexed: 11/18/2022] Open
Abstract
A substantial barrier to the clinical adoption of cuffless blood pressure (BP) monitoring techniques is the lack of unified error standards and methods of estimating measurement uncertainty. This study proposes a fusion approach to improve accuracy and estimate prediction interval (PI) as a proxy for uncertainty for cuffless blood BP monitoring. BP was estimated during activities of daily living using three model architectures: nonlinear autoregressive models with exogenous inputs, feedforward neural network models, and pulse arrival time models. Multiple one-class support vector machine (OCSVM) models were trained to cluster data in terms of the percentage of outliers. New BP estimates were then assigned to a cluster using the OCSVMs hyperplanes, and the PIs were estimated using the BP error standard deviation associated with different clusters. The OCSVM was used to estimate the PI for the three BP models. The three BP estimations from the models were fused using the covariance intersection fusion algorithm, which improved BP and PI estimates in comparison with individual model precision by up to 24%. The employed model fusion shows promise in estimating BP and PI for potential clinical uses. The PI indicates that about 71%, 64%, and 29% of the data collected from sitting, standing, and walking can result in high-quality BP estimates. Our PI estimator offers an effective uncertainty metric to quantify the quality of BP estimates and can minimize the risk of false diagnosis.
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Affiliation(s)
- Cederick Landry
- Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, Canada
| | - Sean D Peterson
- Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, Canada
| | - Arash Arami
- Mechanical and Mechatronics Engineering Department, University of Waterloo, Waterloo, ON, Canada.
- Toronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, Canada.
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He J, Ou J, He A, Shu L, Liu T, Qu R, Xu X, Chen Z, Yan Y. A new approach for daily life Blood-Pressure estimation using smart watch. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103616] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Avolio A, Cox J, Louka K, Shirbani F, Tan I, Qasem A, Butlin M. Challenges Presented by Cuffless Measurement of Blood Pressure if Adopted for Diagnosis and Treatment of Hypertension. Pulse (Basel) 2022; 10:34-45. [PMID: 36660438 PMCID: PMC9843645 DOI: 10.1159/000522660] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 01/22/2023] Open
Abstract
The global health burden presented by hypertension is providing increased motivation for improved means of collection of blood pressure (BP) data. A growing area of research and commercial activity is the use of wearable devices to provide BP data using non-invasive cuffless techniques. The accelerated progress in recent years, particularly relating to connectivity of smartphone technology, has promoted the availability of consumer devices that provide values of BP. The main types of devices are wrist-worn, watch-type devices with sensors that typically record a photoplethysmography (PPG) signal, sometimes also with an electrocardiography (ECG) signal. The general underlying concept of the cuffless BP measurement in most device types is the association of BP and the travel time of the arterial pulse between two locations, determined from the time delay between the ECG and PPG signals. Other methods may involve additional analysis of the PPG waveform features. Experimental data are presented to illustrate the challenges presented by cuffless BP techniques in obtaining reliable BP measurements when the change in BP is caused by different stimuli affecting cardiac and vascular mechanisms. These effects influence the association of the measured and physiological BP change, thus presenting significant challenges and potential limitations in the use of cuffless BP devices for the diagnosis and treatment of hypertension.
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Yao P, Xue N, Yin S, You C, Guo Y, Shi Y, Liu T, Yao L, Zhou J, Sun J, Dong C, Liu C, Zhao M. Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor. IEEE J Biomed Health Inform 2022; 26:3708-3719. [PMID: 35417358 DOI: 10.1109/jbhi.2022.3167059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The cuffless blood pressure monitoring method based on photoplethysmogram (PPG) makes it possible for long-term blood pressure monitoring to prevent and treat cardiovascular and cerebrovascular events. However, the traditional feature extraction is based on two separate sensors, which is inconvenient. In a single sensor measurement, the prediction model based on a single feature group usually does not perform well. This paper presents an artificial neural network (ANN) model for predicting blood pressure based on feature combinations. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm is used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features are extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm is used for regression. Thirty-three subjects distributed in three blood pressure groups were recruited. The proposed method passes the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of 0.03 4.27 mmHg for SBP and 0.01 3.38 for DBP. Moreover, the model can track blood pressure in a long-term range, which demonstrates the robustness of the algorithm. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.
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Hong J, Zheng Y, Wu S, Geng G, Liu Q, Poon CCY. Characterization of the vascular system using overnight wearable-based pulse arrival time and ambulatory blood pressure: A pilot study. Comput Biol Med 2021; 137:104861. [PMID: 34530334 DOI: 10.1016/j.compbiomed.2021.104861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/18/2021] [Accepted: 09/07/2021] [Indexed: 11/27/2022]
Abstract
Pulse arrival time (PAT) has been broadly investigated for its potential for cuffless blood pressure (BP) estimation and ease of measurement by wearable devices. It is also of great significance to explore whether PAT conveys complementary information to BP for vascular health assessment. In this paper, the differences between the 24-h ambulatory BP and wearable-based PAT were compared among 12 young normotensives and 15 elderly hypertensives in terms of the mean and coefficients of variation (CoVs). The correlations of the nocturnal normalized PAT (n-PAT) and BP with two arterial stiffness-related parameters (i.e., the intrinsic elastic modulus E0 and the vascular modulation factor α) estimated by a proposed model-based method were also compared. The results showed that the inter-subject variances of the nocturnal average n-PAT were significantly different between the hypertensives and the normotensives (P < 0.001), and the intra-subject CoVs of the nocturnal n-PAT were also significantly different between the two groups (P < 0.05). However, these findings were not shown in the nocturnal BP. The correlation coefficient between the nocturnal average n-PAT and ln(E0) is larger than that with the nocturnal BP, i.e., 0.91 vs. 0.56. Furthermore, the result also revealed that all the hypertensives receiving antihypertensive medications did not achieve the optimal control of the nocturnal BP while presented diverse arterial stiffness indicated by the nocturnal average n-PAT and ln(E0). It is concluded that wearable-based PAT contains complementary information about the vascular system to the ambulatory BP, which may be useful for designing effective antihypertensive treatments.
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Affiliation(s)
- Jingyuan Hong
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Yali Zheng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China.
| | - Shenghao Wu
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Guoqiang Geng
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, China
| | - Qing Liu
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China
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