A novel fuzzy approach for automatic Brunnstrom stage classification using surface electromyography

Clinical assessment plays a major role in post-stroke rehabilitation programs for evaluating impairment level and tracking recovery progress. Conventionally, this process is manually performed by clinicians using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on fuzzy logic is proposed which automatically evaluates stroke patients' impairment level using single-channel surface electromyography (sEMG) signals and generates objective classification results based on the widely used Brunnstrom stages of recovery. The correlation between stroke-induced motor impairment and sEMG features on both time and frequency domain is investigated, and a specifically designed fuzzy kernel classifier based on geometrically unconstrained membership function is introduced in the study to tackle the challenges in discriminating data classes with complex separating surfaces. Experiments using sEMG data collected from stroke patients have been carried out to examine the validity and feasibility of the proposed method. In order to ensure the generalization capability of the classifier, a cross-validation test has been performed. The results, verified using the evaluation decisions provided by an expert panel, have reached a rate of success of the 92.47%. The proposed fuzzy classifier is also compared with other pattern recognition techniques to demonstrate its superior performance in this application.