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dc.contributor.authorRamezani, Hamidehen
dc.contributor.authorKhaki, Hosseinen
dc.contributor.authorErzin, Enginen
dc.contributor.authorAkan, Ozguren
dc.date.accessioned2018-12-18T00:30:41Z
dc.date.available2018-12-18T00:30:41Z
dc.date.issued2017-07en
dc.identifier.issn1557-170X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287029
dc.description.abstractThe aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMRC) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.
dc.format.mediumPrinten
dc.languageengen
dc.subjectHumansen
dc.subjectParkinson Diseaseen
dc.subjectDisease Progressionen
dc.subjectSeverity of Illness Indexen
dc.subjectSpeechen
dc.subjectVoiceen
dc.titleSpeech features for telemonitoring of Parkinson's disease symptoms.en
dc.typeConference Object
prism.endingPage3805
prism.publicationDate2017en
prism.publicationNameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conferenceen
prism.startingPage3801
prism.volume2017en
dc.identifier.doi10.17863/CAM.34339
dcterms.dateAccepted2017-04-19en
rioxxterms.versionofrecord10.1109/embc.2017.8037685en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2017-07en
dc.contributor.orcidRamezani, Hamideh [0000-0003-3813-5077]
dc.contributor.orcidAkan, Ozgur [0000-0003-2523-3858]
dc.identifier.eissn2694-0604
rioxxterms.typeConference Paper/Proceeding/Abstracten
pubs.funder-project-idEuropean Commission FP7 ERC Consolidator Grant (616922)
rioxxterms.freetoread.startdate2018-07-31


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