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The parametrization of the electro myographic signal an approach based on simulated electro myographic signals



The parametrization of the electro myographic signal an approach based on simulated electro myographic signals



Electromyography and Clinical Neurophysiology 24(1-2): 47-66



The neuromuscular system is considered a dynamic system. A stochastic approach to the problem of clinical diagnosis is presented. The use of simulated EMG [electromyography] signals is adopated in order to test the procedures. Parametric identification is applied to the simulated EMG signals. The following 2 problems are studied diagnostic classification and data compression. Identification with autoregressive 2nd order model, AR (2), provides encouraging results concerning discrimination of the simulated pathologies. Validation of this model is not achieved in any of the simulated cases. Optimum models in the AR and ARMA [autoregressive-moving average] classes are identified. From the parameters of these models and freshly generated white noise, reconstruction of the EMG signal is obtained. Time and frequency domain parameters are computed from the original signal and from the reconstructed one. Excellent agreement is shown for the frequency domain parameters also when they are evaluated from the maximum entropy spectrum. Results obtained for the time domain parameters are less satisfactory although diagnostic yield is proved to be preserved after reconstruction.

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