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 (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=
$0.001< 0.01$
). This research provides a new approach to evaluation and rehabilitation training after stroke, and explicates possible pathomechanisms.