Exact inter-discharge interval distribution of motor unit firing patterns with gamma model.
Inter-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 tak...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 5; pp. 1159 - 1172 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135997078&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135997078 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2019 vid: 57 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135997078 135997078 NLM30685857 10.1007/s11517-018-01947-y NLM30685857 135997078 ppf: 1159 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Exact inter-discharge interval distribution of motor unit firing patterns with gamma model. aug: au: Navallas, Javier Porta, Sonia Malanda, Armando affil: Department of Electric, Electronic and Communication Engineering, Public University of Navarra, 31006, Pamplona, Navarra, Spain sug: subj: Signal Processing, Computer Assisted Electromyography Methods Models, Biological False Negative Results Electromyography Statistics and Numerical Data Probability ab: Inter-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. Graphical Abstract Model of the MU firing pattern generation and detection: fT,1(τ), IDI PDF of the physiological firing pattern; fT(τ), IDI PDF after modeling undetected firings (false negatives); fS(τ), IDI PDF after modeling classification errors (false positives). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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