Feature Extraction and Classification of Neuromuscular Diseases Using Scanning EMG

Artug N. T., BOLAT B., Osman O., Goker I., Tulum G., Baslo M. B.

IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), Alberobello, İtalya, 23 - 25 Haziran 2014, ss.262-265 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/inista.2014.6873628
  • Basıldığı Şehir: Alberobello
  • Basıldığı Ülke: İtalya
  • Sayfa Sayıları: ss.262-265
  • Anahtar Kelimeler: Feature extraction, classification, scanning EMG, neuromuscular diseases, MOTOR UNITS, ELECTROMYOGRAPHY
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.