Publication: Masked least-squares averaging in processing of scanning-EMG recordings with multiple-discharges
| dc.contributor.author | Corera Orzanco, Íñigo | |
| dc.contributor.author | Malanda Trigueros, Armando | |
| dc.contributor.author | Rodríguez Falces, Javier | |
| dc.contributor.author | Navallas Irujo, Javier | |
| dc.contributor.department | Ingeniería Eléctrica y Electrónica | es_ES |
| dc.contributor.department | Ingeniaritza Elektrikoa eta Elektronikoa | eu |
| dc.date.accessioned | 2022-04-27T06:40:56Z | |
| dc.date.available | 2022-04-27T06:40:56Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | 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. | en |
| dc.description.sponsorship | This work has been supported by the Spanish Ministry of Science and Innovation under the project PID2019-109062RB-I00. | en |
| dc.format.extent | 25 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1007/s11517-020-02274-x | |
| dc.identifier.issn | 0140-0118 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/42811 | |
| dc.language.iso | eng | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Medical and Biological Engineering and Computing, 58, 3063–3073 (2020). | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109062RB-I00/ES/ | en |
| dc.relation.publisherversion | https://doi.org/10.1007/s11517-020-02274-x | |
| dc.rights | © International Federation for Medical and Biological Engineering 2020 | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.subject | Electromyography | en |
| dc.subject | Scanning-EMG | en |
| dc.subject | Signal processing | en |
| dc.subject | Motor unit | en |
| dc.title | Masked least-squares averaging in processing of scanning-EMG recordings with multiple-discharges | en |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type | Artículo / Artikulua | es |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
| dc.type.version | Versión aceptada / Onetsi den bertsioa | es |
| dspace.entity.type | Publication | |
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