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dc.creatorMalanda Trigueros, Armandoes_ES
dc.creatorStashuk, Daniel W.es_ES
dc.creatorNavallas Irujo, Javieres_ES
dc.creatorRodríguez Falces, Javieres_ES
dc.creatorRodríguez Carreño, Ignacioes_ES
dc.creatorValle, Césares_ES
dc.creatorGarnés Camarena, Óscares_ES
dc.date.accessioned2023-02-07T07:56:42Z
dc.date.available2023-02-07T07:56:42Z
dc.date.issued2022
dc.identifier.citationMalanda, A., Stashuk, D. W., Navallas, J., Rodríguez-Falces, J., Rodríguez-Carreño, I., Valle, C., & Garnés-Camarena, O. (2022). Automatic jitter measurement in needle-detected motor unit potential trains. Computers in Biology and Medicine, 149, 105973. https://doi.org/10.1016/j.compbiomed.2022.105973en
dc.identifier.issn0010-4825
dc.identifier.urihttps://hdl.handle.net/2454/44662
dc.description.abstractIn an active motor unit (MU), the time intervals between the firings of its muscle fibers vary across successive MU activations. This variability is called jitter and is increased in pathological processes that affect the neuromuscular junctions or terminal axonal segments of MUs. Traditionally, jitter has been measured using single fiber electrodes (SFEs) and a difficult and subjective manual technique. SFEs are expensive and reused, implying a potential risk of patient infection; so, they are being gradually substituted by safer, disposable, concentric needle electrodes (CNEs). As CNEs are larger, voltage contributions from individual fibers of a MU are more difficult to detect, making jitter measurement more difficult. This paper presents an automatic method to estimate jitter from trains of motor unit potentials (MUPs), for both SFE and CNE records. For a MUP train, segments of MUPs generated by single muscle fibers (SF MUP segments) are found and jitter is measured between pairs of these segments. Segments whose estimated jitter values are not reliable, according to several SF MUP segment characteristics, are excluded. The method has been tested in several simulation studies that use mathematical models of muscle fiber potentials. The results are very satisfactory in terms of jitter estimation error (less than 10% in most of the cases studied) and mean number of valid jitter estimates obtained per simulated train (greater than 1.0 in many of the cases and less than 0.5 only in the most complicated). A preliminary study with real signals was also performed, using 19 MUP trains from 3 neuropathic patients. Jitter measurements obtained by the automatic method were compared with those extracted from a commercial system (Keypoint) and the edition and supervision of an expert electromyographer. From these measurements 63% were taken from equivalent interval pair sites within the time span of the MUP trains and, as such, were considered as compatible measurements. Differences in jitter of these compatible measurements were very low (mean value of 1.3 μs, mean of absolute differences of 2.97 μs, 25% and 75% percentile intervals of − 0.85 and 3.82 μs, respectively). Although new tests with larger number of real recordings are still required, the method seems promising for clinical practice.en
dc.description.sponsorshipThis work has been supported by the Spanish Ministry of Science, Education, and Universities, under the “Salvador de Madariaga” 2018 Program and by the Spanish Ministry of Education and Research, under the PID2019-109062RB-I00 project. Open Access funding provided by the Public University of Navarra.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofComputers in Biology and Medicine 149 (2022) 105973en
dc.rights© 2022 The Authors. This is an open access article under the CC BY licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectConcentric needle electrodeen
dc.subjectJitter estimationen
dc.subjectMotor unit potentialen
dc.subjectNeuromuscular disordersen
dc.subjectNeuromuscular transmission instabilityen
dc.subjectSingle fiber potentialen
dc.titleAutomatic jitter measurement in needle-detected motor unit potential trainsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.date.updated2023-02-07T07:44:25Z
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritzaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.1016/j.compbiomed.2022.105973
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109062RB-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.1016/j.compbiomed.2022.105973
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoaes


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© 2022 The Authors. This is an open access article under the CC BY license
Except where otherwise noted, this item's license is described as © 2022 The Authors. This is an open access article under the CC BY license

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