Comperative Methods in Classification of EMG Signals

Yıl 2019, Cilt: 3 Sayı: 2, 205 - 213, 11.02.2020

Ayten Akbay , Oğuz Bayat

Öz

With the development of medical applications, the processing of electromyography signals has gained an
important place in biomedical field. The detection, processing and classification of EMG signals is crucial
because it enables a more standard assessment of different neuromuscular diseases [Kehri et al.( 2016)]. This
article examines neuromuscular diseases based on EMG signals by using classification methods as Multilayer
Perceptron Neural Networks and C4,5 decision tree classifiers. In these methods, an autoregressive (AR) EMG
signal model was used as input to the classification system. 1200 MUAPs data gathered from 7 healthy subjects,
7 myopathy patients and 13 neurogenic patients were analyzed. Total accuracy of Multilayer Perceptron
algorithm is 98.1% and the total accuracy of C4.5 Decision Tree is 94.8%. Comparisons between these two
classifiers are made using a set of scalar performance criteria for classification.

Anahtar Kelimeler

EMG, Neural networks, Decision trees, Myopathy, Neuropathy

Elektromiyografi Sinyallerinin Sınıfılandırılmasında Kullanılan Karşılaştırmalı Metodlar

Öz

Tıbbi uygulamaların gelişmesiyle birlikte elektromiyografi sinyallerinin işlenmesi biyomedikal alanda önemli
bir yer edinmiştir. EMG sinyallerinin tespiti, işlenmesi ve sınıflandırılması farklı nöromüsküler hastalıkların daha
standart bir değerlendirme sağlanması açısından oldukça önemlidir. Bu makale EMG sinyallerine dayanan
nöromüsküler hastalıkları Çok Katmanlı Algı Sinir Ağları ve C4,5 Karar Ağacı sınıflandırma yöntemlerini
kullanarak incelemektedir.

Anahtar Kelimeler

EMG, Yapay Sinir Ağları, Karar Ağacı, Miyopati, Nöropati

Kaynakça

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