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dc.creatorNavallas Irujo, Javieres_ES
dc.creatorPorta Cuéllar, Soniaes_ES
dc.creatorMalanda Trigueros, Armandoes_ES
dc.date.accessioned2019-08-14T11:21:24Z
dc.date.available2019-08-14T11:21:24Z
dc.date.issued2019
dc.identifier.issn0140-0118 (Print)
dc.identifier.issn1741-0444 (Electronic)
dc.identifier.urihttps://hdl.handle.net/2454/34333
dc.description.abstractInter-discharge interval distribution modeling of the motor unit firing pattern plays an important role in electromyographic decomposition and the statistical analysis of firing patterns. When modeling firing patterns obtained from automatic procedures, false positives and false negatives can be taken into account to enhance performance in estimating firing pattern statistics. Available models of this type, however, are only approximate and use Gaussian distributions, which are not strictly suitable for modeling renewal point processes. In this paper, the theory of point processes is used to derive an exact solution to the distribution when a gamma distribution is used to model the physiological firing pattern. Besides being exact, the solution provides a way to model the skewness of the inter-discharge distribution, and this may make it possible to obtain a better fit with available experimental data. In order to demonstrate potential applications of the model, we use it to obtain a maximum likelihood estimator of firing pattern statistics. Our tests found this estimator to be reliable over a wide range of firing conditions, whether dealing with real or simulated firing patterns, the proposed solution had better agreement than other models.en
dc.description.sponsorshipThis work has been supported by the Spanish Ministerio de Economía y Competitividad (MINECO), under the TEC2014-58947-R project.en
dc.format.extent13 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherSpringeren
dc.relation.ispartofMedical and Biological Engineering and Computing (2019) 57:1159–1171en
dc.rights© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectElectromyography (EMG)en
dc.subjectInter-discharge interval (IDI)en
dc.subjectMotor unit firing patternen
dc.subjectMotor unit potential trainen
dc.titleExact inter-discharge interval distribution of motor unit firing patterns with gamma modelen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko, Elektroniko eta Telekomunikazio Sailaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1007/s11517-018-01947-y
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TEC2014-58947-Ren
dc.relation.publisherversionhttps://doi.org/10.1007/s11517-018-01947-y
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Except where otherwise noted, this item's license is described as © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.