Classification Of Emg Signals Using Decision Tree Methods

Classification Of Emg Signals Using Decision Tree Methods

Selami , Keleş

Nowadays, Usage of EMG signals are increasing very fast among the Medical Professionals to determine specific disorders. Recent Computational Intelligence studies show that EMG signals can be processed by machine learning methods. The aim of our study is to implement an accurate system to classify EMG signals using decision tree algorithms. We preprocessed the EMG signals and used autoregressive method (AR) for feature selection. Features are reduced by different filtering methods and applied to decision tree classification algorithms, namely Simple CART, C4.5, Random Forest and Random Tree. EMG signals are classified as Myopathy, Neuropathy and Normal. All the data are compared each other on the table try to find out the best classification and feature reduction methods. While tree algorithms classify the data with the accuracy between %89, 82 and %99, 25, feature reduction slightly affects the accuracy of the classification methods. It has been shown that a successful automatic diagnostic system implemented to classify EMG signals by using decision tree algorithms. Furthermore, future reduction may help to increase the accuracy of the system. Keywords: EMG, Neuropathy, Myopathy, Simple CART, C4.5, Random Tree, Random Forest, Feature reduction.

Conference or Workshop Item
PeerReviewed

2012-05-31

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