A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Muzaffar Khan, Jaikaran Singh, Mukesh Tiwari
2016 International Journal of Computer Applications  
Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy. The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time
more » ... assifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label ( Myopathic , Neuropathic , or Normal ) for a given MUAP. Extensive analysis was performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.
doi:10.5120/ijca2016907710 fatcat:yz3543yp4zhv5fl7naecom6pze