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Cao Z, Ye B, Cao H, Zou Y, Zhu Z, Xing H. Biplane Enhancement Coil for Magnetic Induction Tomography of Cerebral Hemorrhage. BIOSENSORS 2024; 14:217. [PMID: 38785690 PMCID: PMC11117671 DOI: 10.3390/bios14050217] [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: 03/04/2024] [Revised: 04/18/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Magnetic Induction Tomography (MIT) is a non-invasive imaging technique used for dynamic monitoring and early screening of cerebral hemorrhage. Currently, there is a significant challenge in cerebral hemorrhage MIT due to weak detection signals, which seriously affects the accuracy of the detection results. To address this issue, a dual-plane enhanced coil was proposed by combining the target field method with consideration of the spatial magnetic field attenuation pattern within the imaging target region. Simulated detection models were constructed using the proposed coil and cylindrical coil as excitation coils, respectively, and simulation imaging tests were conducted using the detection results. The simulation results indicate that compared to the cylindrical coil, the proposed coil enhances the linearity of the magnetic field within the imaging target region by 60.43%. Additionally, it effectively enhances the detection voltage and phase values. The simulation results of hemorrhage detection show that the proposed coil improves the accuracy of hemorrhage detection by 18.26%. It provides more precise detection results, offering a more reliable solution for cerebral hemorrhage localization and detection.
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Affiliation(s)
- Zhongkai Cao
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; (Z.C.); (H.C.); (H.X.)
| | - Bo Ye
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; (Z.C.); (H.C.); (H.X.)
- Yunnan Key Laboratory of Intelligent Control and Application, Kunming 650500, China
| | - Honggui Cao
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; (Z.C.); (H.C.); (H.X.)
| | - Yangkun Zou
- College of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China;
| | - Zhizhen Zhu
- The First Military Representative Office of the Chongqing Military Representative Bureau of the Army Equipment Department in Kunming, Kunming 650000, China;
| | - Hongbin Xing
- School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; (Z.C.); (H.C.); (H.X.)
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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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Bedolla CN, Gonzalez JM, Vega SJ, Convertino VA, Snider EJ. An Explainable Machine-Learning Model for Compensatory Reserve Measurement: Methods for Feature Selection and the Effects of Subject Variability. Bioengineering (Basel) 2023; 10:bioengineering10050612. [PMID: 37237682 DOI: 10.3390/bioengineering10050612] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Tracking vital signs accurately is critical for triaging a patient and ensuring timely therapeutic intervention. The patient's status is often clouded by compensatory mechanisms that can mask injury severity. The compensatory reserve measurement (CRM) is a triaging tool derived from an arterial waveform that has been shown to allow for earlier detection of hemorrhagic shock. However, the deep-learning artificial neural networks developed for its estimation do not explain how specific arterial waveform elements lead to predicting CRM due to the large number of parameters needed to tune these models. Alternatively, we investigate how classical machine-learning models driven by specific features extracted from the arterial waveform can be used to estimate CRM. More than 50 features were extracted from human arterial blood pressure data sets collected during simulated hypovolemic shock resulting from exposure to progressive levels of lower body negative pressure. A bagged decision tree design using the ten most significant features was selected as optimal for CRM estimation. This resulted in an average root mean squared error in all test data of 0.171, similar to the error for a deep-learning CRM algorithm at 0.159. By separating the dataset into sub-groups based on the severity of simulated hypovolemic shock withstood, large subject variability was observed, and the key features identified for these sub-groups differed. This methodology could allow for the identification of unique features and machine-learning models to differentiate individuals with good compensatory mechanisms against hypovolemia from those that might be poor compensators, leading to improved triage of trauma patients and ultimately enhancing military and emergency medicine.
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Affiliation(s)
- Carlos N Bedolla
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Jose M Gonzalez
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Saul J Vega
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Víctor A Convertino
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- Department of Medicine, Uniformed Services University, Bethesda, MD 20814, USA
- Department of Emergency Medicine, University of Texas Health, San Antonio, TX 78229, USA
- Department of Biomedical Engineering, University of Texas Health, San Antonio, TX 78249, USA
| | - Eric J Snider
- U.S. Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
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