Person:
Malanda Trigueros, Armando

Profile Picture

Email Address

person.page.identifierURI

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

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Malanda Trigueros

First Name

Armando

person.page.departamento

IngenierĆ­a ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n

person.page.instituteName

ISC. Institute of Smart Cities

ORCID

0000-0002-3122-9049

person.page.upna

379

Name

Filters

2018 - 20203
Reset filters

Settings

Subject: Electromyography Ɨ Start date: 2010 Ɨ End date: 2020 Ɨ

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Sliding window averaging in normal and pathological motor unit action potential trains
    (Elsevier, 2018) Malanda Trigueros, Armando; Navallas Irujo, Javier; RodrĆ­guez Falces, Javier; Porta CuĆ©llar, Sonia; FernĆ”ndez MartĆ­nez, Miguel; IngenierĆ­a ElĆ©ctrica, ElectrĆ³nica y de ComunicaciĆ³n; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    Objective: To evaluate the performance of a recently proposed motor unit action potential (MUAP) averaging method based on a sliding window, and compare it with relevant published methods in normal and pathological muscles. Methods: Three versions of the method (with different window lengths) were compared to three relevant published methods in terms of signal analysis-based merit figures and MUAP waveform parameters used in the clinical practice. 218 MUAP trains recorded from normal, myopathic, subacute neurogenic and chronic neurogenic muscles were analysed. Percentage scores of the cases in which the methods obtained the best performance or a performance not significantly worse than the best were computed. Results: For signal processing figures of merit, the three versions of the new method performed better (with scores of 100, 86.6 and 66.7%) than the other three methods (66.7, 25 and 0%, respectively). In terms of MUAP waveform parameters, the new method also performed better (100, 95.8 and 91.7%) than the other methods (83.3, 37.5 and 25%). Conclusions: For the types of normal and pathological muscle studied, the sliding window approach extracted more accurate and reliable MUAP curves than other existing methods. Significance: The new method can be of service in quantitative EMG.
  • Thumbnail Image
    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.