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Malanda Trigueros, Armando

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https://academica-e.unavarra.es/handle/2454/49489

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Malanda Trigueros

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Armando

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IngenierĆ­a ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n

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ISC. Institute of Smart Cities

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0000-0002-3122-9049

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379

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2017 - 20203
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Subject: Scanning-EMG Ɨ Start date: 2017 Ɨ End date: 2020 Ɨ

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings
    (Springer, 2018) Corera Orzanco, ĆĆ±igo; Eciolaza Ferrando, AdriĆ”n; Rubio Zamora, Oliver; Malanda Trigueros, Armando; RodrĆ­guez Falces, Javier; Navallas Irujo, Javier; IngenierĆ­a ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components.
  • Thumbnail Image
    PublicationOpen Access
    Masked least-squares averaging in processing of scanning-EMG recordings with multiple-discharges
    (Springer, 2020) Corera Orzanco, ĆĆ±igo; Malanda Trigueros, Armando; RodrĆ­guez Falces, Javier; Navallas Irujo, Javier; IngenierĆ­a ElĆ©ctrica y ElectrĆ³nica; Ingeniaritza Elektrikoa eta Elektronikoa
    Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked least-squares 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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    PublicationOpen Access
    Motor unit profile: a new way to describe the scanning-EMG potential
    (Elsevier, 2017) Corera Orzanco, ĆĆ±igo; Malanda Trigueros, Armando; RodrĆ­guez Falces, Javier; Porta CuĆ©llar, Sonia; Navallas Irujo, Javier; IngenierĆ­a ElĆ©ctrica y ElectrĆ³nica; Ingeniaritza Elektrikoa eta Elektronikoa
    The motor unit profile, a representation of the trajectories of positive and negative turns of a scanning-EMG signal, is a new way to characterize the motor unit potential. Such characterization allows quantification of the scanning-EMG signal's complexity, which is closely related to the anatomy and physiology of the motor unit. To extract the motor unit profile, an algorithm that detects the turns of the scanning-EMG signal and links them using point-tracking techniques has been developed. The performance of this algorithm is sensitive to three parameters: the turn detection threshold, the maximum tracking interval threshold, and the trajectory purge threshold. Real scanning-EMG signals have been used to analyze the algorithm's behavior and the influence of the algorithm's parameters and to determine which parameter values provide the best performance.