Comparison of single and multiple time delay estimators: application to muscle fiber conduction velocity estimation

Muhammad, Wrya; Meste, Olivier; Rix, Hervé

doi:10.1016/s0165-1684(02)00202-5

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…MUSCLE FIBRE conduction velocity (CV) is a basic physiological parameter and is known to be related to the type and diameter of muscle fibres, ion concentration, pH and motor unit (MU) firing rate (BRODY et al, 1991;HAKANSSON, 1956;MORIMOTO and MASUDA, 1984;NISHIZONO et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

Farina

Muhammad²,

Fortunato³

et al. 2001

Med. Biol. Eng. Comput.

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154

151

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This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 m s(-1); the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 m s(-1) and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.

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Section: Introductionmentioning

confidence: 99%

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

Farina

Muhammad²,

Fortunato³

et al. 2001

Med. Biol. Eng. Comput.

Self Cite

154

151

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show abstract

“…The following model represents an arbitrary number of surface EMG signals detected along the fibre direction (FARINA et al, 2001;MUHAMMAD et al, 2002):…”

Section: Methods Based On the Detection Of More Than Two Signalsmentioning

confidence: 99%

“…The minimisation of the mean square error function (19) leads to the maximum likelihood estimation of delay for one-dimensional, multichannel detection systems. Methods for an iterative, fast detection of the minimum point of the function (19) have been proposed in the literature (FARINA et al, 2001;MUHAMMAD et al, 2002). A multichannel maximum likelihood approach may be useful when the estimation variance should be significantly reduced, for example for appreciating small CV changes over time.…”

Section: Maximum Likelihood Without Constraintsmentioning

confidence: 99%

Methods for estimating muscle fibre conduction velocity from surface electromyographic signals

Farina

Merletti

2004

Med. Biol. Eng. Comput.

131

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The review focuses on the methods currently available for estimating muscle fibre conduction velocity (CV) from surface electromyographic (EMG) signals. The basic concepts behind the issue of estimating CV from EMG signals are discussed. As the action potentials detected at the skin surface along the muscle fibres are, in practice, not equal in shape, the estimation of the delay of propagation (and thus of CV) is not a trivial task. Indeed, a strictly unique definition of delay does not apply in these cases. Methods for estimating CV can thus be seen as corresponding to specific definitions of the delay of propagation between signals of unequal shape. The most commonly used methods for CV estimation are then reviewed. Together with classic methods, recent approaches are presented. The techniques are described with common notations to underline their relationships and to highlight when an approach is a generalisation of a previous one or when it is based on new concepts. The review identifies the difficulties of CV estimation and underlines the issues that should be considered by the investigator when selecting a particular method and detection system for assessing muscle fibre CV. The many open issues in CV estimation are also presented.

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“…Another application is to sort spikes originating from the extracellular activity of different neurons; time alignment is then an important preprocessing step which ensures that spikes with similar shape are assigned to the same cluster [3,4]. Applications of high-resolution time alignment include the estimation of muscle fiber conduction velocity [5], the analysis of PR interval variability in the ECG observed during exercise and recovery [6], and the analysis of QT interval adaptation associated with changes in heart rate [7].…”

Section: Introductionmentioning

confidence: 99%

Eigenvalue-based time delay estimation of repetitive biomedical signals

Laguna

Garde

Giraldo

et al. 2018

Digital Signal Processing

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Comparison of single and multiple time delay estimators: application to muscle fiber conduction velocity estimation

Cited by 11 publications

References 13 publications

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

Methods for estimating muscle fibre conduction velocity from surface electromyographic signals

Eigenvalue-based time delay estimation of repetitive biomedical signals

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