@inproceedings{002f421eed72428e9c9252dab546bc37,
title = "Classification of EMG signals by using AR spectral estimation methods",
abstract = "Quantitative analysis of electromyographic (EMG) signals provides an essential source of information for the diagnosis of neuromuscular disorders. In this study, EMG signals recorded from different subjects were processed using autoregressive methods and EMG power spectra were obtained. The parameters of autoregressive method were estimated by different estimation methods such as Yule-Walker, Burg, covariance and modified covariance. EMG spectra were then used as an input to artificial neural network and compared in relation to their accuracy in classification of EMG signals.",
keywords = "Artificial neural network, Autoregression, EMG, Spectral analysis",
author = "Bozkurt, \{M. R.\} and A. Subasi and E. Koklukaya",
year = "2007",
language = "English",
isbn = "9781601320254",
series = "Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007",
pages = "369--372",
booktitle = "Proceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007",
note = "2007 International Conference on Artificial Intelligence, ICAI 2007 ; Conference date: 25-06-2007 Through 28-06-2007",
}