Feature extraction and classification of neuromuscular diseases using scanning EMG

N. Tuǧrul Artuǧ, Imran Göker, Bülent Bolat, Gökalp Tulum, Onur Osman, M. Baris Baslo

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

10 Atıf (Scopus)

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıINISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings
YayınlayanIEEE Computer Society
Sayfalar262-265
Sayfa sayısı4
ISBN (Basılı)9781479930197
DOI'lar
Yayın durumuYayınlandı - 2014
Harici olarak yayınlandıEvet
Etkinlik2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014 - Alberobello, Italy
Süre: 23 Haz 201425 Haz 2014

Yayın serisi

AdıINISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings

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???event.eventtypes.event.conference???2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA 2014
Ülke/BölgeItaly
ŞehirAlberobello
Periyot23/06/1425/06/14

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