| pubmed-article:17594480 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C0042940 | lld:lifeskim |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C1511790 | lld:lifeskim |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C0034897 | lld:lifeskim |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C0206163 | lld:lifeskim |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C1947916 | lld:lifeskim |
| pubmed-article:17594480 | lifeskim:mentions | umls-concept:C0871161 | lld:lifeskim |
| pubmed-article:17594480 | pubmed:dateCreated | 2007-7-10 | lld:pubmed |
| pubmed-article:17594480 | pubmed:abstractText | Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness. | lld:pubmed |
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| pubmed-article:17594480 | pubmed:language | eng | lld:pubmed |
| pubmed-article:17594480 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17594480 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:17594480 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:17594480 | pubmed:issn | 1475-925X | lld:pubmed |
| pubmed-article:17594480 | pubmed:author | pubmed-author:McSharryPatri... | lld:pubmed |
| pubmed-article:17594480 | pubmed:author | pubmed-author:RobertsStephe... | lld:pubmed |
| pubmed-article:17594480 | pubmed:author | pubmed-author:CostelloDecla... | lld:pubmed |
| pubmed-article:17594480 | pubmed:author | pubmed-author:LittleMax AMA | lld:pubmed |
| pubmed-article:17594480 | pubmed:author | pubmed-author:MorozIrene... | lld:pubmed |
| pubmed-article:17594480 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:17594480 | pubmed:volume | 6 | lld:pubmed |
| pubmed-article:17594480 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:17594480 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:17594480 | pubmed:pagination | 23 | lld:pubmed |
| pubmed-article:17594480 | pubmed:dateRevised | 2010-9-15 | lld:pubmed |
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| pubmed-article:17594480 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:17594480 | pubmed:articleTitle | Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. | lld:pubmed |
| pubmed-article:17594480 | pubmed:affiliation | Systems Analysis, Modelling and Prediction Group, Department of Engineering Science, University of Oxford, Oxford, UK. littlem@robots.ox.ac.uk | lld:pubmed |
| pubmed-article:17594480 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:17594480 | pubmed:publicationType | Comparative Study | lld:pubmed |
| pubmed-article:17594480 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:17594480 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
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