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Gong Z, Lo WLA, Wang R, Li L. Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges. Front Aging Neurosci 2023; 15:1130230. [PMID: 37020859 PMCID: PMC10069712 DOI: 10.3389/fnagi.2023.1130230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.
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Affiliation(s)
- Ze Gong
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Wai Leung Ambrose Lo
- Department of Rehabilitation Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruoli Wang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Le Li
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Le Li,
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Yan H, Yang X, Liu Y, He W, Liao Y, Yang J, Gao Y. Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring. BIOSENSORS 2022; 12:934. [PMID: 36354443 PMCID: PMC9687582 DOI: 10.3390/bios12110934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/17/2022] [Accepted: 10/24/2022] [Indexed: 06/02/2023]
Abstract
The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.
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Affiliation(s)
- Hongli Yan
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
- Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou 350108, China
| | - Xudong Yang
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
- Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou 350108, China
- The School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
| | - Yanyan Liu
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
- Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou 350108, China
- The School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
| | - Wanting He
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
- Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou 350108, China
- The School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
| | - Yipeng Liao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
| | - Jiejie Yang
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
| | - Yueming Gao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- The International Joint Laboratory on Intelligent Health Monitoring Systems, Fuzhou University, Fuzhou 350108, China
- Key Lab of Medical Instrumentation & Pharmaceutical Technology of Fujian Province, Fuzhou 350108, China
- The School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
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