Computer-Aided Estimation of Stroke-Related Upper Extremity Motor Function Using Electroencephalography and Transcranial Magnetic Stimulation

Abstract

Accurate diagnosis of upper extremity motor function in stroke patients is important for effective rehabilitation. However, the approach to correctly perform clinical assessments is still a matter of discussion and requires both trained personnel and specialized materials, thus, limiting the availability of stroke upper extremity diagnosis. Computer-aided methods have been scarcely reported for stroke upper extremity motor function estimation and could support personnel training and clinical decision-making. For these reasons, in the present study, linear regression and regression tree ensembles were applied to estimate upper extremity assessments’ scores using neurophysiological measurements, including electroencephalography (EEG) and transcranial magnetic stimulation (TMS). A database was used to evaluate these approaches and was comprised by measurements of upper extremity sensorimotor and functional performance of stroke patients assessed with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Regression tree ensembles outperformed linear models, estimating 66.7% of the FMA-UE scores and 70% of the ARAT scores with errors below the minimal clinically important difference. The median absolute errors were 3.5 points for the FMA-UE and 1.8 points for the ARAT, within clinically acceptable ranges. Variables that were associated with a higher upper extremity function measured with FMA-UE and ARAT were a higher corticospinal integrity in patients’ affected hemisphere, lower interhemispheric functional connectivity in the central region of the cortex during hand motor intention and, higher alpha activation in the central and lower activation in the parietal regions of the cortex during hand motor intention. Limitations of the study considered, the performance of the proposed approach implied that computer-aided estimation of upper extremity motor function is feasible using physiological information and nonlinear models. These models could be used to create expert systems that support clinical personnel training and decision making regarding upper extremity assessment in stroke.

Keywords

• INDEX TERMS Machine learning
• stroke
• brain-computer interfaces
• transcranial magnetic stimulation
• electroencephalography