Addressing voice recording replications for tracking Parkinson's disease progression.
Tracking Parkinson's disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for eac...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 3; pp. 365 - 374 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2017
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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=121412401&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121412401 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2017 vid: 55 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121412401 121412401 NLM27209185 10.1007/s11517-016-1512-y NLM27209185 121412401 ppf: 365 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Addressing voice recording replications for tracking Parkinson's disease progression. aug: au: Naranjo, Lizbeth Pérez, Carlos Martín, Jacinto Pérez, Carlos J Martín, Jacinto affil: Department of Mathematics , University of Extremadura , Avda. de la Universidad s/n 10003 Cáceres Spain sug: subj: Parkinson Disease Pathology Disease Progression Audiorecording Voice Linear Regression Female Male Databases Reproducibility of Results Female Male ab: Tracking Parkinson's disease symptom severity by using characteristics automatically extracted from voice recordings is a very interesting and challenging problem. In this context, voice features are automatically extracted from multiple voice recordings from the same subjects. In principle, for each subject, the features should be identical at a concrete time, but the imperfections in technology and the own biological variability result in nonidentical replicated features. The involved within-subject variability must be addressed since replicated measurements from voice recordings can not be directly used in independence-based pattern recognition methods as they have been routinely used through the scientific literature. Besides, the time plays a key role in the experimental design. In this paper, for the first time, a Bayesian linear regression approach suitable to handle replicated measurements and time is proposed. Moreover, a version favoring the best predictors and penalizing the worst ones is also presented. Computational difficulties have been avoided by developing Gibbs sampling-based approaches. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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