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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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2
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Yang X, Chen W, Fan Q, Chen J, Chen Y, Lai F, Liu H. Electronic Skin for Health Monitoring Systems: Properties, Functions, and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402542. [PMID: 38754914 DOI: 10.1002/adma.202402542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/22/2024] [Indexed: 05/18/2024]
Abstract
Electronic skin (e-skin), a skin-like wearable electronic device, holds great promise in the fields of telemedicine and personalized healthcare because of its good flexibility, biocompatibility, skin conformability, and sensing performance. E-skin can monitor various health indicators of the human body in real time and over the long term, including physical indicators (exercise, respiration, blood pressure, etc.) and chemical indicators (saliva, sweat, urine, etc.). In recent years, the development of various materials, analysis, and manufacturing technologies has promoted significant development of e-skin, laying the foundation for the application of next-generation wearable medical technologies and devices. Herein, the properties required for e-skin health monitoring devices to achieve long-term and precise monitoring and summarize several detectable indicators in the health monitoring field are discussed. Subsequently, the applications of integrated e-skin health monitoring systems are reviewed. Finally, current challenges and future development directions in this field are discussed. This review is expected to generate great interest and inspiration for the development and improvement of e-skin and health monitoring systems.
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Affiliation(s)
- Xichen Yang
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Wenzheng Chen
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Qunfu Fan
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Jing Chen
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Yujie Chen
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Feili Lai
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
| | - Hezhou Liu
- State Key Lab of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 00240, P. R. China
- Collaborative Innovation Center for Advanced Ship and Dee-Sea Exploration, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, P. R. China
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Valerio A, Demarchi D, O’Flynn B, Motto Ros P, Tedesco S. Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload. SENSORS (BASEL, SWITZERLAND) 2024; 24:3697. [PMID: 38894487 PMCID: PMC11175227 DOI: 10.3390/s24113697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/23/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject's pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.
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Affiliation(s)
- Andrea Valerio
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Danilo Demarchi
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Brendan O’Flynn
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (B.O.); (S.T.)
| | - Paolo Motto Ros
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy;
| | - Salvatore Tedesco
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland; (B.O.); (S.T.)
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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5
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Zhang L, Du W, Kim JH, Yu CC, Dagdeviren C. An Emerging Era: Conformable Ultrasound Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307664. [PMID: 37792426 DOI: 10.1002/adma.202307664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Indexed: 10/05/2023]
Abstract
Conformable electronics are regarded as the next generation of personal healthcare monitoring and remote diagnosis devices. In recent years, piezoelectric-based conformable ultrasound electronics (cUSE) have been intensively studied due to their unique capabilities, including nonradiative monitoring, soft tissue imaging, deep signal decoding, wireless power transfer, portability, and compatibility. This review provides a comprehensive understanding of cUSE for use in biomedical and healthcare monitoring systems and a summary of their recent advancements. Following an introduction to the fundamentals of piezoelectrics and ultrasound transducers, the critical parameters for transducer design are discussed. Next, five types of cUSE with their advantages and limitations are highlighted, and the fabrication of cUSE using advanced technologies is discussed. In addition, the working function, acoustic performance, and accomplishments in various applications are thoroughly summarized. It is noted that application considerations must be given to the tradeoffs between material selection, manufacturing processes, acoustic performance, mechanical integrity, and the entire integrated system. Finally, current challenges and directions for the development of cUSE are highlighted, and research flow is provided as the roadmap for future research. In conclusion, these advances in the fields of piezoelectric materials, ultrasound transducers, and conformable electronics spark an emerging era of biomedicine and personal healthcare.
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Affiliation(s)
- Lin Zhang
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Wenya Du
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jin-Hoon Kim
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Chia-Chen Yu
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Canan Dagdeviren
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
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6
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Avoke D, Elshafeey A, Weinstein R, Kim CH, Martin SS. Digital Health in Diabetes and Cardiovascular Disease. Endocr Res 2024; 49:124-136. [PMID: 38605594 DOI: 10.1080/07435800.2024.2341146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). PURPOSE OF THE REVIEW In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. CONCLUSION We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.
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Affiliation(s)
- Dorothy Avoke
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Robert Weinstein
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Chang H Kim
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S Martin
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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7
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Spatz ES, Ginsburg GS, Rumsfeld JS, Turakhia MP. Wearable Digital Health Technologies for Monitoring in Cardiovascular Medicine. N Engl J Med 2024; 390:346-356. [PMID: 38265646 DOI: 10.1056/nejmra2301903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Affiliation(s)
- Erica S Spatz
- From the Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (E.S.S.); the National Institutes of Health, Bethesda, MD (G.S.G.); the University of Colorado School of Medicine, Aurora (J.S.R.); and Meta Platforms, Menlo Park (J.S.R.), the Stanford Center for Digital Health, Stanford University School of Medicine, Stanford (M.P.T.), and iRhythm Technologies, San Francisco (M.P.T.) - all in California
| | - Geoffrey S Ginsburg
- From the Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (E.S.S.); the National Institutes of Health, Bethesda, MD (G.S.G.); the University of Colorado School of Medicine, Aurora (J.S.R.); and Meta Platforms, Menlo Park (J.S.R.), the Stanford Center for Digital Health, Stanford University School of Medicine, Stanford (M.P.T.), and iRhythm Technologies, San Francisco (M.P.T.) - all in California
| | - John S Rumsfeld
- From the Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (E.S.S.); the National Institutes of Health, Bethesda, MD (G.S.G.); the University of Colorado School of Medicine, Aurora (J.S.R.); and Meta Platforms, Menlo Park (J.S.R.), the Stanford Center for Digital Health, Stanford University School of Medicine, Stanford (M.P.T.), and iRhythm Technologies, San Francisco (M.P.T.) - all in California
| | - Mintu P Turakhia
- From the Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT (E.S.S.); the National Institutes of Health, Bethesda, MD (G.S.G.); the University of Colorado School of Medicine, Aurora (J.S.R.); and Meta Platforms, Menlo Park (J.S.R.), the Stanford Center for Digital Health, Stanford University School of Medicine, Stanford (M.P.T.), and iRhythm Technologies, San Francisco (M.P.T.) - all in California
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8
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Huang B, Hu S, Liu Z, Lin CL, Su J, Zhao C, Wang L, Wang W. Challenges and prospects of visual contactless physiological monitoring in clinical study. NPJ Digit Med 2023; 6:231. [PMID: 38097771 PMCID: PMC10721846 DOI: 10.1038/s41746-023-00973-x] [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: 07/02/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023] Open
Abstract
The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.
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Affiliation(s)
- Bin Huang
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China.
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
| | - Shen Hu
- Department of Obstetrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Epidemiology, The Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zimeng Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Chun-Liang Lin
- College of Electrical Engineering and Computer Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung, Taiwan.
| | - Junfeng Su
- Department of General Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Key Laboratory of Early Warning and Intervention of Multiple Organ Failure, China National Ministry of Education, Hangzhou, Zhejiang, China
| | - Changchen Zhao
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China
| | - Li Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenjin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, 1088 Xueyuan Ave, Nanshan Dist., Shenzhen, Guangdong, China.
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Baker S, Yogavijayan T, Kandasamy Y. Towards Non-Invasive and Continuous Blood Pressure Monitoring in Neonatal Intensive Care Using Artificial Intelligence: A Narrative Review. Healthcare (Basel) 2023; 11:3107. [PMID: 38131997 PMCID: PMC10743031 DOI: 10.3390/healthcare11243107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/29/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 11% of babies are born preterm annually worldwide. Blood pressure (BP) monitoring is essential for managing the haemodynamic stability of preterm infants and impacts outcomes. However, current methods have many limitations associated, including invasive measurement, inaccuracies, and infection risk. In this narrative review, we find that artificial intelligence (AI) is a promising tool for the continuous measurement of BP in a neonatal cohort, based on data obtained from non-invasive sensors. Our findings highlight key sensing technologies, AI techniques, and model assessment metrics for BP sensing in the neonatal cohort. Moreover, our findings show that non-invasive BP monitoring leveraging AI has shown promise in adult cohorts but has not been broadly explored for neonatal cohorts. We conclude that there is a significant research opportunity in developing an innovative approach to provide a non-invasive alternative to existing continuous BP monitoring methods, which has the potential to improve outcomes for premature babies.
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Affiliation(s)
- Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, QLD 4878, Australia
| | - Thiviya Yogavijayan
- College of Medicine and Dentistry, James Cook University, Townsville, QLD 4811, Australia;
| | - Yogavijayan Kandasamy
- Department of Neonatology, Townsville University Hospital, Townsville, QLD 4811, Australia;
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10
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Mohammed H, Chen HB, Li Y, Sabor N, Wang JG, Wang G. Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1257-1281. [PMID: 38015673 DOI: 10.1109/tbcas.2023.3334960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.
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11
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Gunasekaran D, Turner JM. Current and Developing Technologies for BP Monitoring. Curr Cardiol Rep 2023; 25:1151-1156. [PMID: 37698819 DOI: 10.1007/s11886-023-01956-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/28/2023] [Indexed: 09/13/2023]
Abstract
PURPOSE OF REVIEW To discuss new and emerging technologies for blood pressure measurement and monitoring, including the limitations of current blood pressure measurement techniques, hopes for new device technologies, and the current barriers impeding change in this space. RECENT FINDINGS A number of new cuffless devices are being developed and poised to emerge on the marketplace in coming years. There are several different types of technologies and sensors currently under study. New guidelines for validation of cuffless blood pressure devices have recently been developed in anticipation of this change. The current standards for blood pressure device validation are specific to cuff-based technology and are insufficient for validating devices with cuffless-based technologies. In anticipation of a number of new cuffless technologies coming to market in the coming years, three sets of standards have been developed and published in recent years to address this gap.
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Affiliation(s)
| | - Jeffrey M Turner
- Section of Nephrology, Yale School of Medicine, New Haven, CT, USA.
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12
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Delgado-Rivera R, García-Rodríguez W, López L, Cunci L, Resto PJ, Domenech M. PCL/PEO Polymer Membrane Prevents Biofouling in Wearable Detection Sensors. MEMBRANES 2023; 13:728. [PMID: 37623789 PMCID: PMC10456225 DOI: 10.3390/membranes13080728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/27/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
Technological advances in biosensing offer extraordinary opportunities to transfer technologies from a laboratory setting to clinical point-of-care applications. Recent developments in the field have focused on electrochemical and optical biosensing platforms. Unfortunately, these platforms offer relatively poor sensitivity for most of the clinically relevant targets that can be measured on the skin. In addition, the non-specific adsorption of biomolecules (biofouling) has proven to be a limiting factor compromising the longevity and performance of these detection systems. Research from our laboratory seeks to capitalize on analyte selective properties of biomaterials to achieve enhanced analyte adsorption, enrichment, and detection. Our goal is to develop a functional membrane integrated into a microfluidic sampling interface and an electrochemical sensing unit. The membrane was manufactured from a blend of Polycaprolactone (PCL) and Polyethylene oxide (PEO) through a solvent casting evaporation method. A microfluidic flow cell was developed with a micropore array that allows liquid to exit from all pores simultaneously, thereby imitating human perspiration. The electrochemical sensing unit consisted of planar gold electrodes for the monitoring of nonspecific adsorption of proteins utilizing Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). The solvent casting evaporation technique proved to be an effective method to produce membranes with the desired physical properties (surface properties and wettability profile) and a highly porous and interconnected structure. Permeability data from the membrane sandwiched in the flow cell showed excellent permeation and media transfer efficiency with uniform pore activation for both active and passive sweat rates. Biofouling experiments exhibited a decrease in the extent of biofouling of electrodes protected with the PCL/PEO membrane, corroborating the capacity of our material to mitigate the effects of biofouling.
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Affiliation(s)
- Roberto Delgado-Rivera
- Department of Chemical Engineering, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA;
| | - William García-Rodríguez
- Department of Mechanical Engineering, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA; (W.G.-R.); (P.J.R.)
| | - Luis López
- Department of Chemistry, University of Puerto Rico, Río Piedras Campus, San Juan, PR 00925, USA; (L.L.); (L.C.)
| | - Lisandro Cunci
- Department of Chemistry, University of Puerto Rico, Río Piedras Campus, San Juan, PR 00925, USA; (L.L.); (L.C.)
| | - Pedro J. Resto
- Department of Mechanical Engineering, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA; (W.G.-R.); (P.J.R.)
| | - Maribella Domenech
- Department of Chemical Engineering, University of Puerto Rico, Mayagüez Campus, Mayagüez, PR 00680, USA;
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