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dc.contributor.authorLoaiza Duque, Julián David
dc.contributor.authorSánchez Egea, Antonio José
dc.contributor.authorReeb, Theresa
dc.contributor.authorGonzález Rojas, Hernan Alberto
dc.contributor.authorGonzález Vargas, Andrés Mauricio
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Enginyeria Mecànica, Fluids i Aeronàutica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica
dc.date.accessioned2020-05-19T08:58:44Z
dc.date.available2020-05-19T08:58:44Z
dc.date.issued2020-05-11
dc.identifier.citationLoaiza, J. [et al.]. Angular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s Disease and Essential Tremor. "IEEE access", 11 Maig 2020, p. 1-10.
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/2117/188047
dc.description.abstractRecent research has shown that smartphones/smartwatches have a high potential to help physicians to identify and differentiate between different movement disorders. This work aims to develop Machine Learning models to improve the differential diagnosis between patients with Parkinson’s Disease and Essential Tremor. For this purpose, we use a mobile phone’s built-in gyroscope to record the angular velocity signals of two different arm positions during the patient’s follow-up, more precisely, in rest and posture positions. To develop and to find the best classification models, diverse factors were considered, such as the frequency range, the training and testing divisions, the kinematic features, and the classification method. We performed a two-stage kinematic analysis, first to differentiate between healthy and trembling subjects and then between patients with Parkinson’s Disease and Essential Tremor. The models developed reached an average accuracy of 97.2+/-3.7% (98.5% Sensitivity, 93.3% Specificity) to differentiate between Healthy and Trembling subjects and an average accuracy of 77.8+/-9.9% (75.7% Sensitivity, 80.0% Specificity) to discriminate between Parkinson’s Disease and Essential Tremor patients. Therefore, we conclude, that the angular velocity signal can be used to develop Machine Learning models for the differential diagnosis of Parkinson’s disease and Essential Tremor.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshParkinson's disease
dc.subject.lcshMachine learning
dc.subject.otherDifferential diagnosis
dc.subject.otherParkinson’s disease
dc.subject.otherEssential tremor
dc.subject.otherGyroscope
dc.subject.otherKinematic analysis
dc.subject.otherMachine learning.
dc.titleAngular velocity analysis boosted by machine learning for helping in the differential diagnosis of Parkinson’s Disease and Essential Tremor
dc.typeArticle
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacAprenentatge automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. LAM - Laboratori d'Aplicacions Multimèdia i TIC
dc.contributor.groupUniversitat Politècnica de Catalunya. GAECE - Grup d'Accionaments Elèctrics amb Commutació Electrònica
dc.identifier.doi10.1109/ACCESS.2020.2993647
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9091038
dc.rights.accessOpen Access
local.identifier.drac28142065
dc.description.versionPostprint (published version)
local.citation.authorLoaiza, J.; Sanchez Egea, Antonio J.; Reeb, T.; Gonzalez-Rojas, Hernan A.; González Vargas, A.
local.citation.publicationNameIEEE access
local.citation.startingPage1
local.citation.endingPage10


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