Classification of spasticity affected EMG-signals

Abstract

Electromyography (EMG) is used as medical tool to display muscle activity and gain information about the health status of the patients muscle function, which may be affected by many kind of diseases. Spasticity is caused by injuries of the central nervous system, which may occur in consequence of stroke or as concomitant of multiple sclerosis. If the muscle function is influenced by spasticity, there are different types of therapy to regain muscle control. For robotic supported rehabilitation, such as provided by diverse exoskeleton applications, it is important to identify spastic muscle activity patterns, in order to protect patients against mechanical injury. Therefore the EMG data of a hemiplegic patient was analysed, in order to find characteristic features of affected muscle activity and combine them to a characteristic feature vector. To classify the different states of muscle activity a Support Vector Machine (SVM) is used, trained with the feature vector space, which was created from the given EMG data. After that, the developed SVM was applied to data sets of patients also affected by spasticity in order to compare the obtained results to those estimated by a previously used algorithm for spasticity detection. Subsequently, the recognition capability of the implemented SVM was validated by a newly developed EMG sensor node for the IPANEMA Body Sensor Network (BSN).