Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker

Xian Hua; Jing Li; Ting Wang; Junhong Wang; Shaojun Pi; Hangcheng Li; Xugang Xi; Xian Hua; Jing Li; Ting Wang; Junhong Wang; Shaojun Pi; Hangcheng Li; Xugang Xi

doi:10.3934/mbe.2023465

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 10530-10551. doi: 10.3934/mbe.2023465

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Research article

Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker

1.
Jinhua People's Hospital, Jinhua 321000, China
2.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
3.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China
4.
Hangzhou Mingzhou Naokang Rehabilitation Hospital, Hangzhou 311215, China

Received: 11 October 2022 Revised: 14 March 2023 Accepted: 30 March 2023 Published: 10 April 2023

Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.
- stroke,
- electroencephalogram,
- electromyogram,
- dynamic time warping,
- corticomuscular coupling,
- symmetry index
Citation: Xian Hua, Jing Li, Ting Wang, Junhong Wang, Shaojun Pi, Hangcheng Li, Xugang Xi. Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 10530-10551. doi: 10.3934/mbe.2023465

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Abstract

Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.

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