Feature Extraction and Classification of Neuromuscular Diseases Using Scanning EMG - Artug, N. Tugrul; Bolat, Bulent; Osman, Onur; Goker, Imran; Tulum, Gokalp; Baslo, M. Baris (IEEE, 2014)
In this study a new dataset are prepared for neuromuscular diseases using scanning EMG method and four new features are extracted. These features are maximum amplitude, phase duration at the maximum amplitude, maximum amplitude times phase duration, and number of peaks. By using statistical values such as mean and variance, number of features has increased up to eight. This dataset was classified by using multi layer perceptron (MLP), support vector machines (SVM), k-nearest neighbours algorithm (k-NN), and radial basis function networks (RBF). The best accuracy is obtained as 97.78% with SVM algorithm and 3-NN algorithm.
Keywords: Feature extraction; classification; scanning EMG; neuromuscular diseases; ELECTROMYOGRAPHY; Computer Science