A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study
Efficient rehabilitation state evaluation is important to the design of rehabilitation strategies after stroke. However, most traditional evaluations have depended on subjective clinical scales, which do not entail quantitative evaluation of the motor function. Functional corticomuscular coupling (F...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10045695/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849761967223341056 |
|---|---|
| author | Ping Xie Ying Wang Xiaoling Chen Yingying Hao Haoxiang Yang Yinan Yang Meng Xu |
| author_facet | Ping Xie Ying Wang Xiaoling Chen Yingying Hao Haoxiang Yang Yinan Yang Meng Xu |
| author_sort | Ping Xie |
| collection | DOAJ |
| description | Efficient rehabilitation state evaluation is important to the design of rehabilitation strategies after stroke. However, most traditional evaluations have depended on subjective clinical scales, which do not entail quantitative evaluation of the motor function. Functional corticomuscular coupling (FCMC) can be used to quantitatively describe the rehabilitation state. However, how to apply FCMC to clinical evaluation still needs to be studied. In this study, we propose a visible evaluation model which can combine the FCMC indicators with a Ueda score to comprehensively evaluate the motor function. In this model, we first calculated the FCMC indicators based on our previous study, including transfer spectral entropy (TSE), wavelet package transfer entropy (WPTE) and multiscale transfer entropy (MSTE). We then apply Pearson correlation analysis to determine which FCMC indicators are significantly correlated with the Ueda score. Then, we simultaneously introduced a radar map to present the selected FCMC indicators and the Ueda score, and described the relation between them. Finally, we calculated the comprehensive evaluation function (CEF) of the radar map and applied it as a comprehensive score of the rehabilitation state. To verify the model’s effectiveness, we synchronously collected the electroencephalogram (EEG) and electrocardiogram (EMG) data from stroke patients under the steady-state force task and evaluated the state by the model. This model visualized the evaluation results by constructing a radar map and presented the physiological electrical signal features and the clinical scales at the same time. The CEF indicator calculated from this model was significantly correlated with the Ueda score (P=<inline-formula> <tex-math notation="LaTeX">$0.001< 0.01$ </tex-math></inline-formula>). This research provides a new approach to evaluation and rehabilitation training after stroke, and explicates possible pathomechanisms. |
| format | Article |
| id | doaj-art-4a4e89d590c8479fb66d0555bccc3388 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-4a4e89d590c8479fb66d0555bccc33882025-08-20T03:05:52ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01311721173110.1109/TNSRE.2023.324562710045695A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot StudyPing Xie0https://orcid.org/0000-0001-5878-087XYing Wang1Xiaoling Chen2https://orcid.org/0000-0003-3677-3753Yingying Hao3Haoxiang Yang4Yinan Yang5Meng Xu6Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, Qinhuangdao, ChinaKey Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, Qinhuangdao, ChinaKey Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, Qinhuangdao, ChinaKey Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, Qinhuangdao, ChinaDepartment of Neurology, First Hospital of Qinhuangdao, Qinhuangdao, ChinaDepartment of Rehabilitation, First Hospital of Qinhuangdao, Qinhuangdao, ChinaKey Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Hebei, Qinhuangdao, ChinaEfficient rehabilitation state evaluation is important to the design of rehabilitation strategies after stroke. However, most traditional evaluations have depended on subjective clinical scales, which do not entail quantitative evaluation of the motor function. Functional corticomuscular coupling (FCMC) can be used to quantitatively describe the rehabilitation state. However, how to apply FCMC to clinical evaluation still needs to be studied. In this study, we propose a visible evaluation model which can combine the FCMC indicators with a Ueda score to comprehensively evaluate the motor function. In this model, we first calculated the FCMC indicators based on our previous study, including transfer spectral entropy (TSE), wavelet package transfer entropy (WPTE) and multiscale transfer entropy (MSTE). We then apply Pearson correlation analysis to determine which FCMC indicators are significantly correlated with the Ueda score. Then, we simultaneously introduced a radar map to present the selected FCMC indicators and the Ueda score, and described the relation between them. Finally, we calculated the comprehensive evaluation function (CEF) of the radar map and applied it as a comprehensive score of the rehabilitation state. To verify the model’s effectiveness, we synchronously collected the electroencephalogram (EEG) and electrocardiogram (EMG) data from stroke patients under the steady-state force task and evaluated the state by the model. This model visualized the evaluation results by constructing a radar map and presented the physiological electrical signal features and the clinical scales at the same time. The CEF indicator calculated from this model was significantly correlated with the Ueda score (P=<inline-formula> <tex-math notation="LaTeX">$0.001< 0.01$ </tex-math></inline-formula>). This research provides a new approach to evaluation and rehabilitation training after stroke, and explicates possible pathomechanisms.https://ieeexplore.ieee.org/document/10045695/Motor function evaluationfunctional corticomuscular couplingvisible evaluationradar mapstroke |
| spellingShingle | Ping Xie Ying Wang Xiaoling Chen Yingying Hao Haoxiang Yang Yinan Yang Meng Xu A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study IEEE Transactions on Neural Systems and Rehabilitation Engineering Motor function evaluation functional corticomuscular coupling visible evaluation radar map stroke |
| title | A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study |
| title_full | A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study |
| title_fullStr | A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study |
| title_full_unstemmed | A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study |
| title_short | A Multidimensional Visible Evaluation Model for Stroke Rehabilitation: A Pilot Study |
| title_sort | multidimensional visible evaluation model for stroke rehabilitation a pilot study |
| topic | Motor function evaluation functional corticomuscular coupling visible evaluation radar map stroke |
| url | https://ieeexplore.ieee.org/document/10045695/ |
| work_keys_str_mv | AT pingxie amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yingwang amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT xiaolingchen amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yingyinghao amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT haoxiangyang amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yinanyang amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT mengxu amultidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT pingxie multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yingwang multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT xiaolingchen multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yingyinghao multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT haoxiangyang multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT yinanyang multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy AT mengxu multidimensionalvisibleevaluationmodelforstrokerehabilitationapilotstudy |