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dc.contributor.authorLoaiza Duque, Julián D.
dc.contributor.authorGonzález Vargas, Andrés Mauricio
dc.contributor.authorSánchez Egea, Antonio José
dc.contributor.authorGonzález Rojas, Hernan Alberto
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica
dc.date.accessioned2019-12-19T12:53:48Z
dc.date.issued2019-10-09
dc.identifier.citationLoaiza , J. [et al.]. Using machine learning and accelerometry data for differential diagnosis of Parkinson’s disease and essential tremor. A: "Workshop on Engineering Applications". Berlín: Springer, 2019, p. 368-378.
dc.identifier.isbn978-3-030-31019-6
dc.identifier.urihttp://hdl.handle.net/2117/174098
dc.description.abstractParkinson’s disease (PD) and Essential Tremor (ET) are the most common tremor syndromes in the world. Currently, a specific Single Photon Emission Computed Tomography (123I-FP-CIT SPECT) has proven to be an effective tool for the diagnosis of these diseases (97% sensitivity and 100% specificity). However, this test is invasive and expensive, and not all countries can have a SPECT system for an accurate differential diagnosis of PD patients. Clinical evaluation by a neurologist remains the gold standard for PD diagnosis, although the accuracy of this protocol depends on the experience and expertise of the physician. Wearable devices have been found to be a potential tool to help in differential diagnosis of PD and ET in early or complex cases. In this paper, we analyze the linear acceleration of the hand tremor recorded with a built-in accelerometer of a mobile phone, with a sampling frequency of 100 Hz. This hand tremor signal was thoroughly analyzed to extract different kinematic features in the frequency domain. These features were used to explore different Machine Learning methods to automatically classify and differentiate between healthy subjects and hand tremor patients (HETR Group) and, subsequently, patients with PD and ET (ETPD Group). Sensitivity of 90.0% and Specificity of 100.0% were obtained with classifiers of the HETR group. On the other hand, classifiers with Sensitivity ranges from 90.0% to 100.0% and Specificity from 80% to 100% were obtained for the ETPD group. These results indicate that the method proposed can be a potential tool to help the clinicians on differential diagnosis in complex or early hand tremor cases
dc.format.extent11 p.
dc.language.isoeng
dc.publisherSpringer
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica
dc.subject.lcshMachine learning
dc.subject.otherParkinson’s Disease
dc.subject.otherEssential Tremor
dc.subject.otherMachine Learning
dc.subject.otherAccelerometry
dc.subject.otherWearable device
dc.titleUsing machine learning and accelerometry data for differential diagnosis of Parkinson’s disease and essential tremor
dc.typePart of book or chapter of book
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. TECNOFAB - Grup de Recerca en Tecnologies de Fabricació
dc.contributor.groupUniversitat Politècnica de Catalunya. GAECE - Grup d'Accionaments Elèctrics amb Commutació Electrònica
dc.identifier.doi10.1007/978-3-030-31019-6_32
dc.identifier.dlhttps://doi.org/10.1007/978-3-030-31019-6_32
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-31019-6_32
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac25878013
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorLoaiza , J.; González Vargas, A.; Sanchez Egea, Antonio J.; Gonzalez-Rojas, Hernan A.
local.citation.pubplaceBerlín
local.citation.publicationNameWorkshop on Engineering Applications
local.citation.startingPage368
local.citation.endingPage378


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain