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dc.contributor.advisorNowostawski , Mariusz
dc.contributor.advisorFranz, Elizabeth.A
dc.contributor.authorWoods, Alan Michael
dc.date.available2012-10-24T03:08:48Z
dc.date.copyright2012
dc.identifier.citationWoods, A. M. (2012). Differentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis (Thesis, Master of Science). University of Otago. Retrieved from http://hdl.handle.net/10523/2513en
dc.identifier.urihttp://hdl.handle.net/10523/2513
dc.description.abstractSimilarities in the physiological and psychological symptoms of Parkinson’s disease (PD) and Essential Tremor (ET) make accurate diagnosis of PD and ET conditions difficult. It has up to 25% diagnostic error rate. Both disorders have similar postural tremor characteristics, which make it difficult to differentiate on the basis of tremor between the two disorders. Previous studies that have classified PD and ET tremor used multiple neural learning tools and decision support systems to classify between the two conditions. In contrast, we explored the use of Discrete Wavelet Transforms combined with Support Vector Machines and changes in cognitive load as a disorder classification method. Advancements in mobile device technology allow these devices to be used as mobile medical applications. This provides the opportunity for one device to collect, analyse and classify biometric data from a range of disorders. Biometric data has been collected by mobile devices for a number of years, but most analysis and classification have been performed off-line on a central server. In this research, we have concentrated on two questions: Can changes in tremor be used to classify PD and ET postural tremor on a mobile phone? Do the effects of changes in cognitive load through differing levels of attention and distraction affect the level and frequency of tremor differently in PD and ET disorders? We investigate the influence of attention and distraction on tremor by comparingParkinson’s postural tremor with Essential postural tremor as the between-subjects factor and differing attention and distraction levels as within-subjects factors. A primary finding is that in the frequency band directly related to postural tremor in both Essential Tremor and Parkinson’s Disease there is significant difference in the disorders between attention and distraction tasks. These findings suggest that attention and distraction can be successfully used as an input feature space to classify difference in these two disorders. Using the differences between attention and distraction tasks we successfully developed a proof-of-concept smartphone based mobile medical application using discrete wavelet transforms and support vector machine based classification to discriminate between Parkinson’s postural tremor and essential postural tremor. Keywords: Parkinson Disease; Essential Tremor; Wavelet; Support Vector Machine; Attention; Distraction
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherUniversity of Otago
dc.rightsAll items in OUR Archive are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectParkinson Disease
dc.subjectEssential Tremor
dc.subjectWavelet
dc.subjectSupport Vector Machine
dc.subjectAttention
dc.subjectDistraction
dc.subjectParkinson Disease
dc.subjectEssential Tremor
dc.subjectWavelet
dc.subjectSupport Vector Machine
dc.subjectAttention
dc.subjectDistraction
dc.titleDifferentiation of Parkinson’s Tremor from Essential Tremor using Action Tremor Analysis
dc.typeThesis
dc.date.updated2012-10-24T02:05:51Z
dc.language.rfc3066en
thesis.degree.disciplineInformation Science
thesis.degree.nameMaster of Science
thesis.degree.grantorUniversity of Otago
thesis.degree.levelMasters
otago.openaccessOpen
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