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Syst."],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:p> Speech is controlled by axial neuromotor systems, therefore, it is highly sensitive to the effects of neurodegenerative illnesses such as Parkinson\u2019s Disease (PD). Patients suffering from PD present important alterations in speech, which are manifested in phonation, articulation, prosody, and fluency. These alterations may be evaluated using statistical methods on features obtained from glottal, spectral, cepstral, or fractal descriptions of speech. This work introduces an evaluation paradigm based on Information Theory (IT) to differentiate the effects of PD and aging on glottal amplitude distributions. The study is conducted on a database including 48 PD patients (24 males, 24 females), 48 age-matched healthy controls (HC, 24 males, 24 females), and 48 mid-age normative subjects (NS, 24 males, 24 females). It may be concluded from the study that Hierarchical Clustering (HiCl) methods produce a clear separation between the phonation of PD patients from NS subjects (accuracy of 89.6% for both male and female subsets), but the separation between PD patients and HC subjects is less efficient (accuracy of 75.0% for the male subset and 70.8% for the female subset). Conversely, using feature selection and Support Vector Machine (SVM) classification, the differentiation between PD and HC is substantially improved (accuracy of 94.8% for the male subset and 92.8% for the female subset). This improvement was mainly boosted by feature selection, at a cost of information and generalization losses. The results point to the possibility that speech deterioration may affect HC phonation with aging, reducing its difference to PD phonation. <\/jats:p>","DOI":"10.1142\/s0129065720500586","type":"journal-article","created":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T04:38:10Z","timestamp":1594615090000},"page":"2050058","source":"Crossref","is-referenced-by-count":6,"title":["A Methodology to Differentiate Parkinson\u2019s Disease and Aging Speech Based on Glottal Flow Acoustic Analysis"],"prefix":"10.1142","volume":"30","author":[{"given":"Andr\u00e9s","family":"G\u00f3mez-Rodellar","sequence":"first","affiliation":[{"name":"Usher Institute, Medical School, University of Edinburgh, Old Medical School, Teviot Place, Edinburgh, EH8 9AG UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Palacios-Alonso","sequence":"additional","affiliation":[{"name":"Escuela T\u00e9cnica Superior de Ingenier\u00eda Inform\u00e1tica, Universidad Rey Juan Carlos, Calle Tulip\u00e1n, s\/n, 28933 M\u00f3stoles, Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M.","family":"Ferr\u00e1ndez Vicente","sequence":"additional","affiliation":[{"name":"Universidad Polit\u00e9cnica de Cartagena, Campus Universitario Muralla del Mar, Pza. 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