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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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Liu ZD, Li Y, Zhang YT, Zeng J, Chen ZX, Liu JK, Miao F. HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement. IEEE J Biomed Health Inform 2024; 28:3882-3894. [PMID: 38687656 DOI: 10.1109/jbhi.2024.3395445] [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: 05/02/2024]
Abstract
Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.
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Pankaj, Kumar A, Komaragiri R, Kumar M. Blood pressure estimation and classification using a reference signal-less photoplethysmography signal: a deep learning framework. Phys Eng Sci Med 2023; 46:1589-1605. [PMID: 37747644 DOI: 10.1007/s13246-023-01322-8] [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: 01/26/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023]
Abstract
The markers that help to predict th function of a cardiovascular system are hemodynamic parameters like blood pressure (BP), stroke volume, heart rate, and cardiac output. Continuous analysis of hemodynamic parameters such as BP can detect abnormalities earlier, preventing cardiovascular diseases (CVDs). However, sometimes due to motion artifacts, it becomes difficult to monitor the BP accurately and classify it. This work presents an optimized deep learning model having the capability to estimate the systolic blood pressure (SBP) and diastolic blood pressure (DBP) and classify the BP stages simultaneously from the same network using only a single channel photoplethysmography (PPG) signal. The proposed model is designed by exploiting the deep learning framework of a convolutional neural network (CNN), exhibiting the inherent ability to extract features automatically. Moreover, the proposed framework utilizes the superlet transform method to transform a 1-D PPG signal into a 2-D super-resolution time-frequency (TF) spectrogram. A superlet transform separates the peaks related to true PPG signal components and motion artifacts components. Thus, the superlet provides a robust realtime approach to accurately estimating and classifying BP using a single PPG sensor signal and does not require additional ECG and PPG sensor signals for reference. Using a super-resolution spectrogram and CNN model makes the method profitable in motion artifact removal, feature selection, and extraction. Hence the proposed framework becomes less complex for deployment on wearable devices having limited battery resources. The performance of the proposed framework is demonstrated on the publicly available larger dataset MIMIC-III. This work obtained a mean absolute error (MAE) of 2.71 mmHg and 2.42 mmHg for SBP and DBP, respectively. The classification accuracy for the SBP prediction is about 96.79%, whereas it is 98.94% for DBP. From a motion artifact-affected PPG signal, SBP and DBP are estimated. Then the estimated BP is classified into three categories: normotension, prehypertension, and hypertension, and is compared with the state of art methods to show the effectiveness of the proposed optimized framework.
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Affiliation(s)
- Pankaj
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Ashish Kumar
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
- School of Computer science engineering and technology, Bennett University, Greater Noida, India
| | - Rama Komaragiri
- Department of Electronics and Communication Engineering, Bennett University, Greater Noida, India
| | - Manjeet Kumar
- Department of Electronics and Communication Engineering, Delhi Technological University, Delhi, India.
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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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Liu T, Gou GY, Gao F, Yao P, Wu H, Guo Y, Yin M, Yang J, Wen T, Zhao M, Li T, Chen G, Sun J, Ma T, Cheng J, Qi Z, Chen J, Wang J, Han M, Fang Z, Gao Y, Liu C, Xue N. Multichannel Flexible Pulse Perception Array for Intelligent Disease Diagnosis System. ACS NANO 2023; 17:5673-5685. [PMID: 36716225 PMCID: PMC10062340 DOI: 10.1021/acsnano.2c11897] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/23/2023] [Indexed: 05/25/2023]
Abstract
Pressure sensors with high sensitivity, a wide linear range, and a quick response time are critical for building an intelligent disease diagnosis system that directly detects and recognizes pulse signals for medical and health applications. However, conventional pressure sensors have limited sensitivity and nonideal response ranges. We proposed a multichannel flexible pulse perception array based on polyimide/multiwalled carbon nanotube-polydimethylsiloxane nanocomposite/polyimide (PI/MPN/PI) sandwich-structure pressure sensor that can be applied for remote disease diagnosis. Furthermore, we established a mechanical model at the molecular level and guided the preparation of MPN. At the structural level, we achieved high sensitivity (35.02 kPa-1) and a broad response range (0-18 kPa) based on a pyramid-like bilayer microstructure with different upper and lower surfaces. A 27-channel (3 × 9) high-density sensor array was integrated at the device level, which can extract the spatial and temporal distribution information on a pulse. Furthermore, two intelligent algorithms were developed for extracting six-dimensional pulse information and automatic pulse recognition (the recognition rate reaches 97.8%). The results indicate that intelligent disease diagnosis systems have great potential applications in wearable healthcare devices.
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Affiliation(s)
- Tiezhu Liu
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Guang-yang Gou
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Fupeng Gao
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Pan Yao
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Haoyu Wu
- State
Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing10029, China
| | - Yusen Guo
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Minghui Yin
- Department
of Materials and Manufacturing, Beijing
University of Technology, Beijing100124, China
| | - Jie Yang
- TCM
Data Center & Institute of Information on Traditional Chinese
Medicine, China Academy of Chinese Medical
Sciences (CAMS), Beijing100700, China
| | - Tiancai Wen
- TCM
Data Center & Institute of Information on Traditional Chinese
Medicine, China Academy of Chinese Medical
Sciences (CAMS), Beijing100700, China
| | - Ming Zhao
- Department
of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing100853, China
| | - Tong Li
- School
of Modern Post (School of Automation), Beijing
University of Posts and Telecommunications, Beijing100876, China
| | - Gang Chen
- School
of Modern Post (School of Automation), Beijing
University of Posts and Telecommunications, Beijing100876, China
| | - Jianhai Sun
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Tianjun Ma
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Jianqun Cheng
- School
of Integrated Circuit, Quanzhou University
of Information Engineering, Quanzhou, Fujian362000, China
| | - Zhimei Qi
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Jiamin Chen
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Junbo Wang
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
| | - Mengdi Han
- Department
of Biomedical Engineering, College of Future Technology, Peking University, Beijing100091, China
| | - Zhen Fang
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
| | - Yangyang Gao
- State
Key Laboratory of Organic−Inorganic Composites, Beijing University of Chemical Technology, Beijing10029, China
| | - Chunxiu Liu
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
| | - Ning Xue
- School
of Electronic, Electrical, and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing100049, China
- State
Key Laboratory of Transducer Technology, Aerospace Information Research
Institute (AIR), Chinese Academy of Sciences, Beijing100190, China
- Personalized
Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing100190, China
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