title: Myoelectric signal classification using neural networks.
abstract: A feed-forward neural network is used for diagnosis of spastic paralysis. It is a two-layer perceptron and it is able to classify two kinds of myoelectric signal recorded in surface electromyography: the normal EMG and the EMG in the case of spastic paralysis. The myoelectric signal was recorded with a surface electrode pair and sampled at 10 kHz. The EMG activity is stochastic and the instantaneous amplitude distribution for a fixed level of contraction is Gaussian. The signal variance is considered a measure of muscle force. We can describe any kind of this process by the AR model. For a precisely modeling of EMG there are necessary many AR model parameters. In the classification problem we have it is not necessary to use a high order AR model. We find a 4-th order AR model is good enough for this study. The Hopfield algorithm is used to calculate the parameters of the autoregressive model.
authors.affiliation: Politehnica University of Bucharest, Dept. of Applied Electronics, Iuliu Maniu 1-3, Romania.
authors.forename: M
authors.initials: M
authors.lastname: Ungureanu
authors.forename: R
authors.initials: R
authors.lastname: Strungaru
authors.forename: V
authors.initials: V
authors.lastname: Lazarescu
mesh_terms: D004566:Electrodes; D004576:Electromyography; D006429:Hemiplegia / Q000175:diagnosis; D006801:Humans; D016571:Neural Networks, Computer; D012016:Reference Values; D012815:Signal Processing, Computer-Assisted