Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring.
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 38; no. 5; pp. 1163 - 1174 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2024
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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=179949909&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179949909 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Oct2024 vid: 38 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179949909 176422272 10.1007/s10877-024-01141-6 179949909 ppf: 1163 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring. aug: au: Verdonck, Michaël Carvalho, Hugo Fuchs-Buder, Thomas Brull, Sorin J. Poelaert, Jan affil: https://ror.org/00cv9y106 Department of Business Informatics and Operations Management, University Ghent, Tweekerkenstraat 2, 9000, Ghent, Belgium sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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