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Lingawi S, Hutton J, Khalili M, Shadgan B, Christenson J, Grunau B, Kuo C. Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review. Ann Biomed Eng 2024; 52:1136-1158. [PMID: 38358559 DOI: 10.1007/s10439-024-03442-y] [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: 10/20/2023] [Accepted: 01/03/2024] [Indexed: 02/16/2024]
Abstract
Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.
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Affiliation(s)
- Saud Lingawi
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada.
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Centre for Aging SMART, University of British Columbia, 2635 Laurel St., Vancouver, BC, V5Z 1M9, Canada.
| | - Jacob Hutton
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- British Columbia Emergency Health Services, Vancouver, Canada
- Department of Emergency Medicine, University of British Columbia and St. Paul's Hospital, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada
| | - Mahsa Khalili
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- Centre for Aging SMART, University of British Columbia, 2635 Laurel St., Vancouver, BC, V5Z 1M9, Canada
- Department of Emergency Medicine, University of British Columbia and St. Paul's Hospital, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada
| | - Babak Shadgan
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Department of Orthopedic Surgery, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, Vancouver, BC, Canada
| | - Jim Christenson
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- British Columbia Emergency Health Services, Vancouver, Canada
- Department of Emergency Medicine, University of British Columbia and St. Paul's Hospital, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada
| | - Brian Grunau
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- British Columbia Emergency Health Services, Vancouver, Canada
- Department of Emergency Medicine, University of British Columbia and St. Paul's Hospital, Vancouver, BC, Canada
- Centre for Advancing Health Outcomes, University of British Columbia, Vancouver, BC, Canada
| | - Calvin Kuo
- British Columbia Resuscitation Research Collaborative, Vancouver, BC, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
- Centre for Aging SMART, University of British Columbia, 2635 Laurel St., Vancouver, BC, V5Z 1M9, Canada
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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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Boiko A, Gaiduk M, Scherz WD, Gentili A, Conti M, Orcioni S, Martínez Madrid N, Seepold R. Monitoring of Cardiorespiratory Parameters during Sleep Using a Special Holder for the Accelerometer Sensor. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115351. [PMID: 37300078 DOI: 10.3390/s23115351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 06/12/2023]
Abstract
Sleep is extremely important for physical and mental health. Although polysomnography is an established approach in sleep analysis, it is quite intrusive and expensive. Consequently, developing a non-invasive and non-intrusive home sleep monitoring system with minimal influence on patients, that can reliably and accurately measure cardiorespiratory parameters, is of great interest. The aim of this study is to validate a non-invasive and unobtrusive cardiorespiratory parameter monitoring system based on an accelerometer sensor. This system includes a special holder to install the system under the bed mattress. The additional aim is to determine the optimum relative system position (in relation to the subject) at which the most accurate and precise values of measured parameters could be achieved. The data were collected from 23 subjects (13 males and 10 females). The obtained ballistocardiogram signal was sequentially processed using a sixth-order Butterworth bandpass filter and a moving average filter. As a result, an average error (compared to reference values) of 2.24 beats per minute for heart rate and 1.52 breaths per minute for respiratory rate was achieved, regardless of the subject's sleep position. For males and females, the errors were 2.28 bpm and 2.19 bpm for heart rate and 1.41 rpm and 1.30 rpm for respiratory rate. We determined that placing the sensor and system at chest level is the preferred configuration for cardiorespiratory measurement. Further studies of the system's performance in larger groups of subjects are required, despite the promising results of the current tests in healthy subjects.
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Affiliation(s)
- Andrei Boiko
- Ubiquitous Computing Lab, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, 78462 Konstanz, Germany
| | - Maksym Gaiduk
- Ubiquitous Computing Lab, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, 78462 Konstanz, Germany
| | - Wilhelm Daniel Scherz
- Ubiquitous Computing Lab, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, 78462 Konstanz, Germany
| | - Andrea Gentili
- Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Massimo Conti
- Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Simone Orcioni
- Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy
| | | | - Ralf Seepold
- Ubiquitous Computing Lab, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, 78462 Konstanz, Germany
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Boivin V, Shahriari M, Faure G, Mellul S, Tiassou ED, Jouvet P, Noumeir R. Multimodality Video Acquisition System for the Assessment of Vital Distress in Children. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115293. [PMID: 37300019 DOI: 10.3390/s23115293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/23/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
In children, vital distress events, particularly respiratory, go unrecognized. To develop a standard model for automated assessment of vital distress in children, we aimed to construct a prospective high-quality video database for critically ill children in a pediatric intensive care unit (PICU) setting. The videos were acquired automatically through a secure web application with an application programming interface (API). The purpose of this article is to describe the data acquisition process from each PICU room to the research electronic database. Using an Azure Kinect DK and a Flir Lepton 3.5 LWIR attached to a Jetson Xavier NX board and the network architecture of our PICU, we have implemented an ongoing high-fidelity prospectively collected video database for research, monitoring, and diagnostic purposes. This infrastructure offers the opportunity to develop algorithms (including computational models) to quantify vital distress in order to evaluate vital distress events. More than 290 RGB, thermographic, and point cloud videos of each 30 s have been recorded in the database. Each recording is linked to the patient's numerical phenotype, i.e., the electronic medical health record and high-resolution medical database of our research center. The ultimate goal is to develop and validate algorithms to detect vital distress in real time, both for inpatient care and outpatient management.
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Affiliation(s)
- Vincent Boivin
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Electrical Engineering, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
| | - Mana Shahriari
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Pediatrics, Université de Montréal (UdeM), Montréal, QC H3T 1C5, Canada
| | - Gaspar Faure
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
| | - Simon Mellul
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
| | | | - Philippe Jouvet
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Pediatrics, Université de Montréal (UdeM), Montréal, QC H3T 1C5, Canada
| | - Rita Noumeir
- CHU Sainte-Justine Research Centre, Montréal, QC H3T 1C5, Canada
- Department of Electrical Engineering, Ecole de Technologie Supérieure (ETS), Montréal, QC H3C 1K3, Canada
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Boiko A, Martínez Madrid N, Seepold R. Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115038. [PMID: 37299762 DOI: 10.3390/s23115038] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
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Affiliation(s)
- Andrei Boiko
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
| | - Natividad Martínez Madrid
- Internet of Things Laboratory, School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany
| | - Ralf Seepold
- Ubiquitous Computing Laboratory, Department of Computer Science, HTWG Konstanz-University of Applied Sciences, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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Chen Q, Wang Y, Liu X, Long X, Yin B, Chen C, Chen W. Camera-based heart rate estimation for hospitalized newborns in the presence of motion artifacts. Biomed Eng Online 2021; 20:122. [PMID: 34863194 PMCID: PMC8642856 DOI: 10.1186/s12938-021-00958-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 11/15/2021] [Indexed: 02/07/2023] Open
Abstract
Background Heart rate (HR) is an important vital sign for evaluating the physiological condition of a newborn infant. Recently, for measuring HR, novel RGB camera-based non-contact techniques have demonstrated their specific superiority compared with other techniques, such as dopplers and thermal cameras. However, they still suffered poor robustness in infants’ HR measurements due to frequent body movement. Methods This paper introduces a framework to improve the robustness of infants’ HR measurements by solving motion artifact problems. Our solution is based on the following steps: morphology-based filtering, region-of-interest (ROI) dividing, Eulerian video magnification and majority voting. In particular, ROI dividing improves ROI information utilization. The majority voting scheme improves the statistical robustness by choosing the HR with the highest probability. Additionally, we determined the dividing parameter that leads to the most accurate HR measurements. In order to examine the performance of the proposed method, we collected 4 hours of videos and recorded the corresponding electrocardiogram (ECG) of 9 hospitalized neonates under two different conditions—rest still and visible movements. Results Experimental results indicate a promising performance: the mean absolute error during rest still and visible movements are 3.39 beats per minute (BPM) and 4.34 BPM, respectively, which improves at least 2.00 and 1.88 BPM compared with previous works. The Bland-Altman plots also show the remarkable consistency of our results and the HR derived from the ground-truth ECG. Conclusions To the best of our knowledge, this is the first study aimed at improving the robustness of neonatal HR measurement under motion artifacts using an RGB camera. The preliminary results have shown the promising prospects of the proposed method, which hopefully reduce neonatal mortality in hospitals.
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Affiliation(s)
- Qiong Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yalin Wang
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xiangyu Liu
- School of Art Design and Media, East China University of Science and Technology, Shanghai, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Bin Yin
- Connected Care and Personal Health Department, Philips Research, Shanghai, China
| | - Chen Chen
- Human Phenome Institute, Fudan University, Shanghai, China.
| | - Wei Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai, China.
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