Classification of EMG signals by k-Nearest Neighbor algorithm and Support vector machine methods

Electromyography (EMG) is a medical measurement system. EMG measurements are required for the diagnosis of some diseases and used in order to facilitate physicians´ work. In this study, MUAPs´ in an EMG data set that contains both healthy and Amyotrophic Lateral Sclerosis (ALS) disease subjects are represented in time domain and frequency domain with a total of 10 feature vectors. Two pattern recognition methods, namely k-Nearest Neighbor (k-NN) and Support vector machine (SVM) classifier are employed and compared. In terms of classification accuracy, k-NN classifier give slightly higher success rate than SVM classifier for the existing data set and feature vectors.