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

Wang, Shouyan; Liu, Xuguang; Yianni, John; Aziz, Tipu Z.; Stein, John

doi:10.1016/j.jneumeth.2004.04.024

Cited by 38 publications

(16 citation statements)

References 26 publications

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“…Different wavelet transforms are generated from a basic function (mother wavelet function), by scaling and translating. In DWT, scales and positions are based on power of two called dyadic scales and positions (Daubechies 1992;Wang et al 2004). DWT application on a signal at its first step produces two sets of coefficients -approximation and detail coefficients.…”

Section: Discussionmentioning

confidence: 99%

“…The discrete wavelet transform (DWT) became a widespread tool for analyzing localized variations of power spectra within time-frequency variation (Daubechies 1992;Morlet et al 1993;Torrence and Compo 1998;Sprato et al 2000;Wang et al 2004). DWT as a method of multi-resolution analysis has been used successfully for processing the occurrence of frequency alterations in biological signals such as EMG of compound potentials (Daubechies 1992;Coorevits et al 2008), EEG (Subasi et al 2005), ECG (Froesea et al 2006) caused by different physical and chemical influences.…”

Section: Introductionmentioning

confidence: 99%

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Three methods for estimation of changes in frequency characteristics of potentials elicited by long-lasting (fatiguing) activity of isolated muscle fibres

Vukova¹,

Dimitrov²,

Radicheva³

2010

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Abstract. The present study was assigned to compare the applicability and sensitivity of three different methods (discrete wavelet transforms (DWT), median frequency (MDF) -calculated on the basis of fast Fourier transform, and spectral indices) for analysis of frequency content changes in potentials produced by repetitive stimulation from isolated slow (SMFs)-and fast (FMFs)-fatigable muscle fibers during uninterrupted activity. In order to affect fatigue, prior to starting the fatiguing stimulation (5 Hz), some of the fibres were exposed to microwave electromagnetic field and others were sham exposed. All the methods studied demonstrated that during a long-lasting fibre activity, the shift of the potential frequency content to lower frequencies was a consequence of lowering in higher frequency components and increasing in low frequency ones. The changes were faster in FMFs than SMFs and stronger in sham exposed fibres vs. microwave exposed. The MDF alterations in both fibre types and protocols were quite similar to other methods characterizing either individual changes in low and high frequency scales of the signal (discrete wavelet transform -DWT) or changes in their spectral ratio (spectral indices). The spectral indices showed sensitivity to fatigue effect at the final stages that made their changes considerably non-linear. DWT allowed processing the temporal occurrence of frequency content changes that corresponded to the alterations in the separate potential phases as well as earlier detection and precise evaluation of fatigue onset and progressing.All methods are applicable but DWT is a preferable for study of muscle fibre fatigue in clinical and experimental neurophysiology.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three methods for estimation of changes in frequency characteristics of potentials elicited by long-lasting (fatiguing) activity of isolated muscle fibres

Vukova¹,

Dimitrov²,

Radicheva³

2010

gpb

View full text Add to dashboard Cite

show abstract

“…This increases analysis time and introduces subjectivity and dependence upon operator skill. Wang et al (2004) presented a wavelet approach for separating the raw EMG signal into tonic and phasic components. Further signal processing would still be required to quantify amplitude modulation.…”

Section: Comparison To Other Signal Processing Methodsmentioning

confidence: 99%

Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

Belavý

Mehnert

Wilson

et al. 2009

Journal of Electromyography and Kinesiology

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Section: Wavelet Filter Selectionmentioning

confidence: 99%

“…One way to select the wavelet filter is based on minimising entropy which indicates the concentration of the energy [6]. Another empirical way to select a wavelet filter is based on the waveform of the signal and the properties of the wavelet [6]. In the present study, the Symlets wavelet filter was empirically selected for their properties of (1) orthogonal transform, (2) compact support, and (3) optimal number of vanishing moments.…”

Section: Wavelet Filter Selectionmentioning

confidence: 99%

Multivariate Bayesian classification of tongue movement ear pressure signals based on the wavelet packet transform

Mamun

Mace

Lutmen

et al. 2010

2010 IEEE International Workshop on Machine Learning for Signal Processing

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Tongue movement ear pressure signals have been used to generate controlling commands in human-machine interfaces. The objective of this study is to classify the controlled movement relating to an intended action from interfering signals that can be experienced. These interfering signals include but are not limited to, speech, coughing and drinking. Thus data was collected for six types of controlled movement and the various interfering signals, when subjects spoke, coughed or drank. The signal processing involves detection, segmentation, feature extraction and selection, and classification of tongue motions. The segmented signals were initially transformed into the wavelet packet domain, allowing for various features to be extracted based on statistical properties of the wavelet coefficients. These are then used as input into a Bayesian classifier under multivariate Gaussian assumptions. The average classification performance for identifying controlled movements and interfering tongue signals achieved 98% and 93.5% respectively. Thus the classification of tongue movement ear pressure signals based on the wavelet packet transform is robust. The application of this Bayesian classification strategy significantly reduces the interference of controlling commands when considered within a human-machine interface system operating in a challenging environment.Index Terms-Tongue movement ear pressure signals, wavelet packet transform, Bayesian classifier.

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

Cited by 38 publications

References 26 publications

Three methods for estimation of changes in frequency characteristics of potentials elicited by long-lasting (fatiguing) activity of isolated muscle fibres

Three methods for estimation of changes in frequency characteristics of potentials elicited by long-lasting (fatiguing) activity of isolated muscle fibres

Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

Multivariate Bayesian classification of tongue movement ear pressure signals based on the wavelet packet transform

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