Prognosis of stroke upper limb recovery with physiological variables using regression tree ensembles

Abstract

Abstract Objective. This study assesses upper limb recovery prognosis after stroke with solely physiological information, which can provide an objective estimation of recovery. Approach. Clinical recovery was forecasted using EEG-derived Event-Related Desynchronization/Synchronization and coherence, in addition to Transcranial Magnetic Stimulation elicited motor-evoked potentials and upper limb grip and pinch strength. A Regression Tree Ensemble predicted clinical recovery of a stroke database (n = 10) measured after a two-month intervention with the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE) and the Action Research Arm Test (ARAT). Main results. There were no significant differences between predicted and actual outcomes with FMA-UE (p = 0.29) and ARAT (p = 0.5). Median prediction error for FMA-UE and ARAT were of 0.3 (IQR = 6.2) and 3.4 (IQR = 9.4) points, respectively. Predictions with the most pronounced errors were due to an underestimation of high upper limb recovery. The best features for FMA-UE prediction included mostly beta activity over the sensorimotor cortex. Best ARAT prediction features were cortical beta activity, corticospinal tract integrity of the unaffected hemisphere, and upper limb strength. Significance. Results highlighted the importance of measuring cortical activity related to motor control processes, the unaffected hemisphere’s integrity, and upper limb strength for prognosis. It was also implied that stroke upper limb recovery prediction is feasible using solely physiological variables with a Regression Tree Ensemble, which can also be used to analyze physiological relationships with recovery.