A hierarchical ensemble model for automated assessment of stroke impairment

Assessment of sensory, motor and cognitive function of stroke subjects provide important information to guide patient rehabilitation. As many of the currently used measures are inherently subjective and use course rating scales, here we propose a hierarchical ensemble network that can automatically identify stroke patients and assess their upper limb functionality objectively, based on experimental task data. We compare our neural network ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it significantly outperforms competitors. We also demonstrate that our measure scale is congruent with clinical information, responsive with changes of patients motor function, and reliable in terms of test-retest configuration.