Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The compound surface electromyograms (EMGs) recorded from patients with dystonia commonly contains superimposed bursting and tonic activity representing various motor symptoms. It is desirable to differentially extract them from the compound EMGs so that different symptoms can be more specifically investigated and different mechanisms revealed. A non-linear denoising approach based on wavelet transformation was investigated by applying soft thresholding to the wavelet coefficients. Thresholds were determined according to three different principles and two models. Different techniques for wavelet shrinkage were investigated for separating burst and tonic activity in the compound EMGs. The combination of Stein's unbiased risk estimate principle with a non-white noise model proved optimal for separating burst and tonic activity. These turned out to be exponentially related; and the temporal relationships between antagonist muscle contractions could now be seen clearly. We conclude that adaptive soft-thresholding wavelet shrinkage provides effective separation of burst and tonic activity in the compound EMG in dystonia. This separation should improve our understanding of the pathophysiology of dystonia.

Original publication




Journal article


J Neurosci Methods

Publication Date





177 - 184


Action Potentials, Dystonia, Electric Stimulation, Electromyography, Humans, Muscle, Skeletal, Nonlinear Dynamics