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Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage

Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage
Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage
The compound surface 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.
electromyogram, wavelet transform, thresholding, dystonia
0165-0270
177-184
Wang, Shou-Yan
f08e1454-91d3-4403-b4b6-d1d9eba6961b
Liu, Xuguang
82f0b077-5b67-495b-92be-5cf1ed8d7bb1
Yianni, John
940fddfe-1c78-4845-9d1a-3d667d27be72
Aziz, Tipu Z.
84768d79-fc87-4c3e-8955-d2e72ca5e6a0
Stein, John F.
341274f8-3eee-4614-958c-635e0b498d78
Wang, Shou-Yan
f08e1454-91d3-4403-b4b6-d1d9eba6961b
Liu, Xuguang
82f0b077-5b67-495b-92be-5cf1ed8d7bb1
Yianni, John
940fddfe-1c78-4845-9d1a-3d667d27be72
Aziz, Tipu Z.
84768d79-fc87-4c3e-8955-d2e72ca5e6a0
Stein, John F.
341274f8-3eee-4614-958c-635e0b498d78

Wang, Shou-Yan, Liu, Xuguang, Yianni, John, Aziz, Tipu Z. and Stein, John F. (2004) Extracting burst and tonic components from surface electromyograms in dystonia using adaptive wavelet shrinkage. Journal of Neuroscience Methods, 139 (2), 177-184. (doi:10.1016/j.jneumeth.2004.04.024).

Record type: Article

Abstract

The compound surface 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.

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More information

Published date: 2004
Keywords: electromyogram, wavelet transform, thresholding, dystonia
Organisations: Human Sciences Group

Identifiers

Local EPrints ID: 46230
URI: https://eprints.soton.ac.uk/id/eprint/46230
ISSN: 0165-0270
PURE UUID: 90e3ee91-5d50-4d33-af60-9cf3afef73a4

Catalogue record

Date deposited: 11 Jun 2007
Last modified: 13 Mar 2019 21:03

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