A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Corera, Íñigo; Eciolaza, Adrian; Rubio, Oliver; Malanda, Armando; Rodriguez‐Falces, Javier; Navallas, Javier

doi:10.1007/s11517-017-1773-0

Cited by 3 publications

(33 citation statements)

References 30 publications

(44 reference statements)

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“…In the algorithm, the simulated SFAP is considered to be a function of the following parameters ̂( ) =̂( , , , Δ , ∆ 0 ) (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) where is the muscle fiber diameter, is the radial fiber-electrode distance, Δ is the axial distance between the IZ and the electrode position, and ∆ 0 is the unknown time offset between the origin of the time axis in the recording and the actual potential initiation instant. Note that and are specific parameters of each MU fiber, while Δ and ∆ 0 are global parameters.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…In order to obtain realistic signals, in which artifacts from other active MUs are present, the entire recording procedure of the scanning-EMG signal is simulated [17]. At each position of the scanning electrode, the EMG pattern is calculated for a certain percentage of MVC, and a trace of a limited duration is extracted from the EMG for each firing event of the MU.…”

Section: Modeling the Scanning-emg Recordingmentioning

confidence: 99%

“…In this chapter, different algorithms developed during this thesis to process and analyze electromyographic signals are described [16,17,18]. Some of these algorithms are important pieces of the estimation system developed to solve the inverse problem.…”

Section: Signal Processingmentioning

confidence: 99%

“…A median filter (usually of 3, 5 or 7 points) [42,44,90] can be used for this purpose. However, this filter noticeably distorts the waveform of the scanning signal [17]. Phenomena such as peak clipping [42,44,90] or stepping of the amplitude profile in the spatial dimension [17] have been reported when the median filter is used.…”

Section: Spacementioning

confidence: 99%

“…As explained in previous sections, a median filter in the spatial dimension is commonly applied to remove artifacts in scanning-EMG recordings. However, this filter has the drawback that it significantly distorts the waveform of the signal [42,44,90,17]. To overcome this limitation, we developed a new algorithm for artifact removal, the masked least-squares smoothing (MLSS) algorithm [17].…”

Section: Masked Least-squares Smoothing Algorithmmentioning

confidence: 99%

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Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Corera¹,

Rodriguez‐Falces²

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In this thesis, several algorithmic procedures to estimate the MU fiber parameters from the waveform of a scanning-EMG signal (i.e., from the recording of a MUP in multiple positions along a linear corridor) have been developed. The different procedures correspond to different modifications of the scanning-EMG technique, which differ in the number of scanning needles and recording ports used to record the signal. The three proposed variants are: the 1-port recording, based on a single scanning needle with a single recording port placed at one side of the needle (i.e., a single fiber EMG needle); the 2-port recording, based on a single scanning needle, with two recording ports, placed at opposite sides of the needle; and the 4-port recording, based on two scanning needles, each one with two recording ports. The estimation system also uses a linear array of surface-EMG recordings to obtain complementary information about the MU. In this way, the recording setup to achieve the MU parameter estimation consists on a simultaneous surface- and scanning-EMG signal recording of the MUP. The estimation system has been evaluated for the three scanning-EMG recording configurations (1-port, 2-port, and 4-port), and compared to the case in which the estimation is performed from a MUP recorded at a single position. The evaluation has been done in a simulation framework, using state of the art models of the muscle, MUs, recruitment, and needles, and developing specific models for the simultaneous surface- and scanning-EMG recording process. This provides a controlled environment in which the performance of the system can be objectively quantified and evaluated.The results evidence that MU parameters are estimated much more accurately when using the scanning-EMG technique than when using a MUP recorded at a single position, corroborating the hypothesis that the use of signals recorded at multiple positions enhances the parameter estimation. Among the three proposed recording configurations, the poorest estimation results have been obtained for the 1-port configuration which, moreover, is only capable of estimating the MU fibers at one side of the needle. The 2-port configuration gives better results, and allows to estimate the MU fibers at both sides of the needle. The 4-port configuration is the one that provides the best performance, but it has the disadvantage of being the most difficult configuration to be physically implemented. In the view of these results, a deeper evaluation of the 2-port recording configuration is done. This is because it combines a good estimation performance with a relative ease to be physically implemented. An additional effort is done to calculate several global MU parameters, such as the MU fiber density, the average potential propagation velocity of the fibers, and the width of the innervation zone, from the resulting set of estimated fibers. These global MU parameters provide relevant physiological information from a clinical point of view. Hence global parameters connect the estimation system developed in this thesis with a future application in the diagnosis and follow-up of neuromuscular pathologies.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Modeling the Scanning-emg Recordingmentioning

confidence: 99%

Section: Signal Processingmentioning

confidence: 99%

Section: Spacementioning

confidence: 99%

Section: Masked Least-squares Smoothing Algorithmmentioning

confidence: 99%

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Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Corera¹,

Rodriguez‐Falces²

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Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Corera

Malanda

Rodriguez‐Falces

et al. 2020

Med Biol Eng Comput

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Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked leastsquares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension.We refer to this algorithm as the multi masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. Results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.

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EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern

Navallas

Eciolaza

Mariscal

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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An analytical derivation of the EMG signal's amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal buildsup, or fills, as the degree of muscle contraction increases. The EMG PDF is found to change from a semi-degenerate distribution to a Laplacian-like distribution and finally to a Gaussian-like distribution. We present a measure, the EMG filling factor, to quantify the degree to which an EMG signal has been built-up. This factor is calculated from the ratio of two non-central moments of the rectified EMG signal. The curve of the EMG filling factor as a function of the mean rectified amplitude shows a progressive and mostly linear increase during early recruitment, and saturation is observed when the EMG signal distribution becomes approximately Gaussian. Having presented the analytical tools used to derive the EMG PDF, we demonstrate the usefulness of the EMG filling factor and curve in studies with both simulated signals and real signals obtained from the tibialis anterior muscle of 10 subjects. Both simulated and real EMG filling curves start within the 0.2 to 0.35 range and rapidly rise towards 0.5 (Laplacian) before stabilizing at around 0.637 (Gaussian). Filling curves for the real signals consistently followed this pattern (100% repeatability within trials in 100% of the subjects). The theory of EMG signal filling derived in this work provides (a) an analytically consistent derivation of the EMG PDF as a function of motor unit potentials and motor unit firing patterns; (b) an explanation of the change in the EMG PDF according to degree of muscle contraction; and (c) a way (the EMG filling factor) to quantify the degree to which an EMG signal has been built-up.

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A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings

Cited by 3 publications

References 30 publications

Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Estimation of the motor unit structure from scanning electromyography and surface electromyography recordings

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

EMG Probability Density Function: A New Way to Look at EMG Signal Filling From Single Motor Unit Potential to Full Interference Pattern

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