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Lambert TP, Chan M, Sanchez-Perez JA, Nikbakht M, Lin DJ, Nawar A, Bashar SK, Kimball JP, Zia JS, Gazi AH, Cestero GI, Corporan D, Padala M, Hahn JO, Inan OT. A Comparison of Normalization Techniques for Individual Baseline-Free Estimation of Absolute Hypovolemic Status Using a Porcine Model. BIOSENSORS 2024; 14:61. [PMID: 38391980 PMCID: PMC10886994 DOI: 10.3390/bios14020061] [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: 11/21/2023] [Revised: 01/07/2024] [Accepted: 01/16/2024] [Indexed: 02/24/2024]
Abstract
Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10-3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10-3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.
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Affiliation(s)
- Tamara P. Lambert
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
| | - Michael Chan
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
| | - Jesus Antonio Sanchez-Perez
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Mohammad Nikbakht
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - David J. Lin
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Afra Nawar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Syed Khairul Bashar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Jacob P. Kimball
- The Donald P. Shiley School of Engineering, University of Portland, Portland, OR 97203, USA;
| | - Jonathan S. Zia
- Division of Neurology & Neurological Sciences, Stanford School of Medicine, Palo Alto, CA 94304, USA;
| | - Asim H. Gazi
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA 02134, USA;
| | - Gabriela I. Cestero
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
| | - Daniella Corporan
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GA 30308, USA; (D.C.); (M.P.)
- Division of Cardiothoracic Surgery, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Muralidhar Padala
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GA 30308, USA; (D.C.); (M.P.)
- Division of Cardiothoracic Surgery, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;
| | - Omer T. Inan
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.C.); (O.T.I.)
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (J.A.S.-P.); (M.N.); (D.J.L.); (A.N.); (S.K.B.); (G.I.C.)
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2
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Feng C, Yang L. State of the art, trends, hotspots, and prospects of injection materials for controlling bleeding. Int Wound J 2024; 21:e14644. [PMID: 38272794 PMCID: PMC10789653 DOI: 10.1111/iwj.14644] [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: 11/26/2023] [Accepted: 12/18/2023] [Indexed: 01/27/2024] Open
Abstract
Traumatic haemorrhage is a prevalent clinical condition, and effective and timely haemostasis is crucial for the preservation of patients' lives. In recent years, injectable hemostatic materials have gained significant attention due to their excellent hemostatic efficacy, biocompatibility, and biodegradability, making them widely applied in the treatment of incompressible traumatic haemorrhage. Systematic analysis of injectable hemostatic materials is crucial for research in this area. This article provides a comprehensive review of the development and research trends of injectable hemostatic materials over the past 20 years using visualization techniques. Analysis of collaboration and co-citation networks revealed localized research collaboration networks, highlighting the need for enhanced international collaboration in the field of injectable hemostatic materials. Current research focuses primarily on hemostatic materials, hemostatic processes, and hemostatic mechanisms. Injectable hemostatic materials with excellent performance offer promising strategies for wound healing. This review provides a comprehensive and systematic summary of injectable hemostatic materials, offering valuable guidance for the development and clinical application of novel injectable hemostatic materials. Additionally, visualized methodology and mapping analysis are effective data mining methods that provide approaches and strategies for clear knowledge network analysis. These methods facilitate better understanding and interpretation of research dynamics in the field of injectable hemostatic materials, thereby guiding and inspiring future research.
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Affiliation(s)
- Changsheng Feng
- School of Physics and Electronic InformationYan'an UniversityYan'anChina
| | - Liang Yang
- School of Physics and Electronic InformationYan'an UniversityYan'anChina
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3
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Wang H, Yang L. Applications of injectable hemostatic materials in wound healing: principles, strategies, performance requirements, and future perspectives. Theranostics 2023; 13:4615-4635. [PMID: 37649606 PMCID: PMC10465227 DOI: 10.7150/thno.86930] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/09/2023] [Indexed: 09/01/2023] Open
Abstract
Uncontrolled traumatic bleeding can lead to death due to excessive blood loss within minutes. Early intervention is crucial to save lives, making timely and effective hemostasis is a major global challenge. Injectable hemostatic materials (IHMs) have been proposed to improve the effectiveness of hemostasis, facilitate wound healing, and enhance survival rates in emergency situations. The superior hemostatic performance of IHMs has garnered significant attention. However, there are relatively few comprehensive reviews on IHMs. This paper aims to provide a comprehensive review of the latest research progress on IHMs in recent years. Firstly, the physiological hemostatic process and the underlying principles of hemostasis are analyzed. Subsequently, the synthesis strategies for different IHMs are discussed. The performance requirements of IHMs are then summarized, including high efficiency, biocompatibility, degradability, manipulability, stability and antibacterial ability. Finally, the development prospects and challenges of IHMs are presented. This review serves as a necessary and systematic summary of IHMs, providing a valuable reference for the development of new high-performance hemostatic materials and their practical clinical applications.
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Affiliation(s)
| | - Liang Yang
- School of Physics and Electronic Information, Yan'an University, Yan'an, 716000, China
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4
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Lin DJ, Gazi AH, Kimball J, Nikbakht M, Inan OT. Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models. IEEE J Biomed Health Inform 2023; 27:3889-3899. [PMID: 37155395 DOI: 10.1109/jbhi.2023.3273989] [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: 05/10/2023]
Abstract
Wearable systems can provide accurate cardiovascular evaluations by estimating hemodynamic indices in real-time. Key hemodynamic parameters can be non-invasively estimated using the seismocardiogram (SCG), a cardiomechanical signal whose features link to cardiac events like aortic valve opening (AO) and closing (AC). However, tracking a single SCG feature is unreliable due to physiological changes, motion artifacts, and external vibrations. This work proposes an adaptable Gaussian Mixture Model (GMM) to track multiple AO/AC correlated features in quasi-real-time from the SCG. The GMM calculates the likelihood of an extremum being an AO/AC feature for each SCG beat. The Dijkstra algorithm selects heartbeat-related extrema, and a Kalman filter updates the GMM parameters while filtering features. Tracking accuracy is tested on a porcine hypovolemia dataset with varying noise levels. Blood volume loss estimation accuracy is also evaluated using the tracked features on a previously developed model. Experimental results show a 4.5 ms tracking latency and average root mean square errors (RMSE) of 1.47 ms for AO and 7.67 ms for AC at 10 dB noise, and 6.18 ms for AO and 15.3 ms for AC at -10 dB noise. When considering all AO/AC correlated features, the combined RMSE remains in similar ranges, specifically 2.70 ms for AO and 11.91 ms for AC at 10 dB noise, and 7.50 ms for AO and 16.35 ms for AC at -10 dB noise. The proposed algorithm offers low latency and RMSE for all tracked features, making it suitable for real-time processing. These systems enable accurate, timely extraction of hemodynamic indices for many cardiovascular monitoring applications, including trauma care in field settings.
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Rahman MM, Cook J, Taebi A. Non-contact heart vibration measurement using computer vision-based seismocardiography. Sci Rep 2023; 13:11787. [PMID: 37479720 PMCID: PMC10362031 DOI: 10.1038/s41598-023-38607-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/11/2023] [Indexed: 07/23/2023] Open
Abstract
Seismocardiography (SCG) is the noninvasive measurement of local vibrations of the chest wall produced by the mechanical activity of the heart and has shown promise in providing clinical information for certain cardiovascular diseases including heart failure and ischemia. Conventionally, SCG signals are recorded by placing an accelerometer on the chest. In this paper, we propose a novel contactless SCG measurement method to extract them from chest videos recorded by a smartphone. Our pipeline consists of computer vision methods including the Lucas-Kanade template tracking to track an artificial target attached to the chest, and then estimate the SCG signals from the tracked displacements. We evaluated our pipeline on 14 healthy subjects by comparing the vision-based SCG[Formula: see text] estimations with the gold-standard SCG[Formula: see text] measured simultaneously using accelerometers attached to the chest. The similarity between SCG[Formula: see text] and SCG[Formula: see text] was measured in the time and frequency domains using the Pearson correlation coefficient, a similarity index based on dynamic time warping (DTW), and wavelet coherence. The average DTW-based similarity index between the signals was 0.94 and 0.95 in the right-to-left and head-to-foot directions, respectively. Furthermore, SCG[Formula: see text] signals were utilized to estimate the heart rate, and these results were compared to the gold-standard heart rate obtained from ECG signals. The findings indicated a good agreement between the estimated heart rate values and the gold-standard measurements (bias = 0.649 beats/min). In conclusion, this work shows promise in developing a low-cost and widely available method for remote monitoring of cardiovascular activity using smartphone videos.
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Affiliation(s)
- Mohammad Muntasir Rahman
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, 39762, USA
| | - Jadyn Cook
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, 39762, USA
| | - Amirtahà Taebi
- Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi, 39762, USA.
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6
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Siecinski S, Tkacz EJ, Grzegorzek M. Publicly available signal databases containing seismocardiographic signals - the state in early 2023. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083212 DOI: 10.1109/embc40787.2023.10340318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023. Based on Google search results for the expression "seismocardiography dataset", we have found and described five databases with seismocardiograms, including three databases that contain SCG signals from healthy subjects, one database with data from porcine subjects, and one signal database with data obtained from human patients with valvular heart disease (VHD). All contain additional signals for reference points in the cardiac cycle. The most significant limitations of the described data sets are gender bias toward male subjects, the imbalance between healthy subjects, and subjects with two cardiovascular diseases (VHD and hemorrhage).
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7
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Deng L, Wang L, Li L, Gong Z, Wang R, Fei W, Zhou Y, Wang F. Bioabsorbable Fibrillar Gauze Dressing Based on N-Carboxyethyl Chitosan Gelling Fibers for Fatal Hemorrhage Control. ACS APPLIED BIO MATERIALS 2023; 6:899-907. [PMID: 36691985 DOI: 10.1021/acsabm.2c01089] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Death from lethal hemorrhage remains a major problem in various emergency scenarios. There is a continuous interest in the development of absorbable hemostatic dressings that can control hemorrhage rapidly and can be left in the wound site without removal. In this study, we report a hemostatic gauze dressing based on N-carboxyethyl chitosan (CECS) gelling fibers. The CECS fibrillar gauze combines ultra-hydrophilic, cationic chemical property of the fiber components with the fluffy nonwoven material form, exhibiting good conformability for wound filling, high fluid uptaking capacity, and enhanced blood-concentrating effect. In a swine femoral artery injury model, the CECS fibrillar gauze achieves shorter time to hemostasis and less blood loss compared with commercially available hemostatic dressings. This chitosan gelling fiber gauze demonstrates comparable bioabsorbability to clinically used absorbable hemostat and thus may be applied to treat fatal hemorrhage both in emergency medical services and in internal surgical procedures.
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Affiliation(s)
- Lili Deng
- Department of Critical Care Medicine, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 201620, China
| | - Liu Wang
- Department of Critical Care Medicine, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 201620, China
| | - Li Li
- Department of Orthopaedics and Traumatology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China
| | - Zuguang Gong
- College of Materials Science and Engineering and Key Laboratory of Green Processing and Functional Textiles of New Textile Materials, Ministry of Education, Wuhan Textile University, Wuhan 430073, China
| | - Ruilan Wang
- Department of Critical Care Medicine, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 201620, China
| | - Wendi Fei
- Department of Orthopaedics and Traumatology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China
| | - Yingshan Zhou
- College of Materials Science and Engineering and Key Laboratory of Green Processing and Functional Textiles of New Textile Materials, Ministry of Education, Wuhan Textile University, Wuhan 430073, China
| | - Fang Wang
- Department of Orthopaedics and Traumatology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai 200120, China
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8
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Zhang X, Shi L, Xiao W, Wang Z, Wang S. Design of Adhesive Hemostatic Hydrogels Guided by the Interfacial Interactions with Tissue Surface. ADVANCED NANOBIOMED RESEARCH 2022. [DOI: 10.1002/anbr.202200115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Xiaobin Zhang
- Key Laboratory of Bio-inspired Materials and Interface Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P.R. China
- University of Chinese Academy of Sciences Beijing 100049 P.R. China
| | - Lianxin Shi
- Key Laboratory of Bio-inspired Materials and Interface Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P.R. China
- Binzhou Institute of Technology Binzhou 256600 P.R. China
| | - Wuyi Xiao
- Key Laboratory of Bio-inspired Materials and Interface Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P.R. China
- University of Chinese Academy of Sciences Beijing 100049 P.R. China
| | - Zhao Wang
- Key Laboratory of Bio-inspired Materials and Interface Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P.R. China
| | - Shutao Wang
- Key Laboratory of Bio-inspired Materials and Interface Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P.R. China
- University of Chinese Academy of Sciences Beijing 100049 P.R. China
- Qingdao Casfuture Research Institute Co. Ltd Qingdao 266109 P.R. China
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9
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Zheng Y, Wu J, Zhu Y, Wu C. Inorganic-based biomaterials for rapid hemostasis and wound healing. Chem Sci 2022; 14:29-53. [PMID: 36605747 PMCID: PMC9769395 DOI: 10.1039/d2sc04962g] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022] Open
Abstract
The challenge for the treatment of severe traumas poses an urgent clinical need for the development of biomaterials to achieve rapid hemostasis and wound healing. In the past few decades, active inorganic components and their derived composites have become potential clinical products owing to their excellent performances in the process of hemorrhage control and tissue repair. In this review, we provide a current overview of the development of inorganic-based biomaterials used for hemostasis and wound healing. We highlight the methods and strategies for the design of inorganic-based biomaterials, including 3D printing, freeze-drying, electrospinning and vacuum filtration. Importantly, inorganic-based biomaterials for rapid hemostasis and wound healing are presented, and we divide them into several categories according to different chemistry and forms and further discuss their properties, therapeutic mechanisms and applications. Finally, the conclusions and future prospects are suggested for the development of novel inorganic-based biomaterials in the field of rapid hemostasis and wound healing.
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Affiliation(s)
- Yi Zheng
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences No. 1295 Dingxi Road Shanghai 200050 People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences No. 19(A) Yuquan Road Beijing 100049 People's Republic of China
| | - Jinfu Wu
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences No. 1295 Dingxi Road Shanghai 200050 People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences No. 19(A) Yuquan Road Beijing 100049 People's Republic of China
| | - Yufang Zhu
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences No. 1295 Dingxi Road Shanghai 200050 People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences No. 19(A) Yuquan Road Beijing 100049 People's Republic of China
| | - Chengtie Wu
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences No. 1295 Dingxi Road Shanghai 200050 People's Republic of China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences No. 19(A) Yuquan Road Beijing 100049 People's Republic of China
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10
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Closed-Loop Controlled Fluid Administration Systems: A Comprehensive Scoping Review. J Pers Med 2022; 12:jpm12071168. [PMID: 35887665 PMCID: PMC9315597 DOI: 10.3390/jpm12071168] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 02/07/2023] Open
Abstract
Physiological Closed-Loop Controlled systems continue to take a growing part in clinical practice, offering possibilities of providing more accurate, goal-directed care while reducing clinicians’ cognitive and task load. These systems also provide a standardized approach for the clinical management of the patient, leading to a reduction in care variability across multiple dimensions. For fluid management and administration, the advantages of closed-loop technology are clear, especially in conditions that require precise care to improve outcomes, such as peri-operative care, trauma, and acute burn care. Controller design varies from simplistic to complex designs, based on detailed physiological models and adaptive properties that account for inter-patient and intra-patient variability; their maturity level ranges from theoretical models tested in silico to commercially available, FDA-approved products. This comprehensive scoping review was conducted in order to assess the current technological landscape of this field, describe the systems currently available or under development, and suggest further advancements that may unfold in the coming years. Ten distinct systems were identified and discussed.
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11
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Wajdan A, Jahren TS, Villegas-Martinez M, Khan FH, Halvorsen PS, Odland HH, Elle OJ, Solberg AHS, Remme EW. Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer. IEEE J Biomed Health Inform 2022; 26:4450-4461. [PMID: 35679388 DOI: 10.1109/jbhi.2022.3181148] [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: 11/09/2022]
Abstract
BACKGROUND Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction. We tested if deep learning can be used to detect these valve events from epicardially attached accelerometers, using high fidelity pressure measurements to establish ground truth for these valve events. METHOD A deep neural network consisting of a CNN, an RNN, and a multi-head attention module was trained and tested on 130 recordings from 19 canines and 159 recordings from 27 porcines covering different interventions. Due to limited data, nested cross-validation was used to assess the accuracy of the method. RESULT The correct detection rates were 98.9% and 97.1% for AVO and AVC in canines and 98.2% and 96.7% in porcines when defining a correct detection as a prediction closer than 40 ms to the ground truth. The incorrect detection rates were 0.7% and 2.3% for AVO and AVC in canines and 1.1% and 2.3% in porcines. The mean absolute error between correct detections and their ground truth was 8.4 ms and 7.2 ms for AVO and AVC in canines, and 8.9 ms and 10.1 ms in porcines. CONCLUSION Deep neural networks can be used on signals from epicardially attached accelerometers for robust and accurate detection of the opening and closing of the aortic valve.
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Chalumuri YR, Kimball JP, Mousavi A, Zia JS, Rolfes C, Parreira JD, Inan OT, Hahn JO. Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:1336. [PMID: 35214238 PMCID: PMC8963055 DOI: 10.3390/s22041336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 12/15/2022]
Abstract
This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jacob P. Kimball
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Azin Mousavi
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jonathan S. Zia
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Christopher Rolfes
- Global Center for Medical Innovation, Translational Training and Testing Laboratories, Inc. (T3 Labs), Atlanta, GA 30313, USA;
| | - Jesse D. Parreira
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Omer T. Inan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
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13
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Wearable Sensors and Machine Learning for Hypovolemia Problems in Occupational, Military and Sports Medicine: Physiological Basis, Hardware and Algorithms. SENSORS 2022; 22:s22020442. [PMID: 35062401 PMCID: PMC8781307 DOI: 10.3390/s22020442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 12/14/2021] [Accepted: 12/30/2021] [Indexed: 11/16/2022]
Abstract
Hypovolemia is a physiological state of reduced blood volume that can exist as either (1) absolute hypovolemia because of a lower circulating blood (plasma) volume for a given vascular space (dehydration, hemorrhage) or (2) relative hypovolemia resulting from an expanded vascular space (vasodilation) for a given circulating blood volume (e.g., heat stress, hypoxia, sepsis). This paper examines the physiology of hypovolemia and its association with health and performance problems common to occupational, military and sports medicine. We discuss the maturation of individual-specific compensatory reserve or decompensation measures for future wearable sensor systems to effectively manage these hypovolemia problems. The paper then presents areas of future work to allow such technologies to translate from lab settings to use as decision aids for managing hypovolemia. We envision a future that incorporates elements of the compensatory reserve measure with advances in sensing technology and multiple modalities of cardiovascular sensing, additional contextual measures, and advanced noise reduction algorithms into a fully wearable system, creating a robust and physiologically sound approach to manage physical work, fatigue, safety and health issues associated with hypovolemia for workers, warfighters and athletes in austere conditions.
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Guo B, Dong R, Liang Y, Li M. Haemostatic materials for wound healing applications. Nat Rev Chem 2021; 5:773-791. [PMID: 37117664 DOI: 10.1038/s41570-021-00323-z] [Citation(s) in RCA: 326] [Impact Index Per Article: 108.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 12/12/2022]
Abstract
Wounds are one of the most common health issues, and the cost of wound care and healing has continued to increase over the past decade. The first step in wound healing is haemostasis, and the development of haemostatic materials that aid wound healing has accelerated in the past 5 years. Numerous haemostatic materials have been fabricated, composed of different active components (including natural polymers, synthetic polymers, silicon-based materials and metal-containing materials) and in various forms (including sponges, hydrogels, nanofibres and particles). In this Review, we provide an overview of haemostatic materials in wound healing, focusing on their chemical design and operation. We describe the physiological process of haemostasis to elucidate the principles that underpin the design of haemostatic wound dressings. We also highlight the advantages and limitations of the different active components and forms of haemostatic materials. The main challenges and future directions in the development of haemostatic materials for wound healing are proposed.
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Prehospital Hemorrhage Assessment Criteria: A Concise Review. J Trauma Nurs 2021; 28:332-338. [PMID: 34491952 DOI: 10.1097/jtn.0000000000000608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Early assessment of the clinical status of trauma patients is crucial for guiding the treatment strategy, and it requires a rapid and systematic approach. The aim of this report is to critically review the assessment parameters currently used in the prehospital setting to quantify blood loss in trauma. DATA SOURCES Studies regarding hemorrhagic shock in trauma were pooled from PubMed, EMBASE, and Cochrane databases using key words such as "hemorrhagic shock," "vital signs evaluation," "trauma," "blood loss," and "emergency medical service," alone or combined. STUDY SELECTION Articles published since 2009 in English and Italian were considered eligible if containing data on assessment parameters in blood loss in adults. DATA EXTRACTION Sixteen articles matching the inclusion criteria were considered in our study. DATA SYNTHESIS Current prehospital assessment measures lack precise correlation with blood loss. CONCLUSIONS Traditional assessment parameters such as heart rate, systolic blood pressure, shock index, and Glasgow Coma Scale score often lag in providing accurate blood loss assessment. The current literature supports the need for a noninvasive, continuously monitored assessment parameter to identify early shock in the prehospital setting.
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Kimball JP, Zia JS, An S, Rolfes C, Hahn JO, Sawka MN, Inan OT. Unifying the Estimation of Blood Volume Decompensation Status in a Porcine Model of Relative and Absolute Hypovolemia Via Wearable Sensing. IEEE J Biomed Health Inform 2021; 25:3351-3360. [PMID: 33760744 DOI: 10.1109/jbhi.2021.3068619] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Hypovolemia remains the leading cause of preventable death in trauma cases. Recent research has demonstrated that using noninvasive continuous waveforms rather than traditional vital signs improves accuracy in early detection of hypovolemia to assist in triage and resuscitation. This work evaluates random forest models trained on different subsets of data from a pig model (n = 6) of absolute (bleeding) and relative (nitroglycerin-induced vasodilation) progressive hypovolemia (to 20% decrease in mean arterial pressure) and resuscitation. Features for the models were derived from a multi-modal set of wearable sensors, comprised of the electrocardiogram (ECG), seismocardiogram (SCG) and reflective photoplethysmogram (RPPG) and were normalized to each subject.s baseline. The median RMSE between predicted and actual percent progression towards cardiovascular decompensation for the best model was 30.5% during the relative period, 16.8% during absolute and 22.1% during resuscitation. The least squares best fit line over the mean aggregated predictions had a slope of 0.65 and intercept of 12.3, with an R2 value of 0.93. When transitioned to a binary classification problem to identify decompensation, this model achieved an AUROC of 0.80. This study: a) developed a global model incorporating ECG, SCG and RPPG features for estimating individual-specific decompensation from progressive relative and absolute hypovolemia and resuscitation; b) demonstrated SCG as the most important modality to predict decompensation; c) demonstrated efficacy of random forest models trained on different data subsets; and d) demonstrated adding training data from two discrete forms of hypovolemia increases prediction accuracy for the other form of hypovolemia and resuscitation.
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Lin DJ, Kimball JP, Zia J, Ganti VG, Inan OT. Reducing the Impact of External Vibrations on Fiducial Point Detection in Seismocardiogram Signals. IEEE Trans Biomed Eng 2021; 69:176-185. [PMID: 34161234 DOI: 10.1109/tbme.2021.3090376] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. METHODS The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. RESULTS SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5\% for pre-ejection period (PEP), 67.2\% for left ventricular ejection time (LVET), and 57.7\% for PEP/LVET---a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. CONCLUSION These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. SIGNIFICANCE Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings.
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Convertino VA, Schauer SG, Weitzel EK, Cardin S, Stackle ME, Talley MJ, Sawka MN, Inan OT. Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6413. [PMID: 33182638 PMCID: PMC7697670 DOI: 10.3390/s20226413] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 12/21/2022]
Abstract
Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.
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Affiliation(s)
- Victor A. Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
| | - Steven G. Schauer
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Erik K. Weitzel
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- 59th Medical Wing, JBSA Lackland, San Antonio, TX 78236, USA
| | - Sylvain Cardin
- Navy Medical Research Unit, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Mark E. Stackle
- Commander, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Michael J. Talley
- Commanding General, US Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA;
| | - Michael N. Sawka
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
| | - Omer T. Inan
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
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