Effective use of EMG parameters in the assessment of neuromuscular diseases

Optimal autoregressive orders for myopathic electromyograms

This paper aims to describe the optimal autoregressive order of varying-length electromyograms for myopathic subjects. Epochs of electromyography signals are modeled as outputs of autoregressive systems, for orders varying from 1 to 100. The optimal order to represent each epoch is chosen by the minimum description length criterion. Probability density functions are fitted to the histograms of the optimal orders. The lognormal function provides the best fitting, and its mean value varies linearly with the epoch length.

Epoch length and autoregressive-order selection for electromyography signals

This study shows how different EMG-epoch lengths affect the selection of the autoregressive-model orders. Electromyography signals were divided in 25ms, 50ms, 100ms, 250ms and 500ms epochs. Order-selection criteria were applied to the least-square errors of autoregressive models. The Bayesian Information Criterion and the Minimum Description Length indicated that needle-EMG signals recorded from normal subjects at 25kHz could be represented by autoregressive models using orders below 25 for 500ms epochs, and that smaller orders could be used to represent shorter epochs.