Comperative Methods in Classification of EMG Signals

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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