Visualização e classificação de características para a discriminação entre indivíduos com a doença de Parkinson submetidos a tratamento com Levodopa e estimulação profunda do cérebro

• Ano de defesa: 2017
• Autor(a) principal: Machado, Alessandro Ribeiro de Pádua
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Engenharia elétrica; Parkinson, Doença de; Estimulação Profunda do Cérebro; Sensores inerciais; Eletromiografia; Parkinson’s disease; Deep Brain Stimulation; Levodopa; Inertial sensors; Electromyography; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/18291; http://dx.doi.org/10.14393/ufu.te.2017.49

Over the years, a number of distinct treatments have been adopted for the management of the motor symptoms of Parkinson’s disease (PD), including pharmacologic therapies and deep brain stimulation (DBS). Efficacy is most often evaluated by subjective assessments which are prone to error and dependent on the experience of the examiner. Our goal was to identify an objective means of assessing response to therapy. In this study, we employed objective analyses in order to visualize and identify differences between three groups: healthy control (N=10), subjects with PD treated with DBS (N=12), and subjects with PD treated with levodopa (N=16). Subjects were assessed during execution of three dynamic tasks (finger taps, finger to nose, supination and pronation) and a static task (rest, i.e., extended arm with no active movement). Measurements were acquired with two pairs of inertial and electromyographic sensors. Feature extraction was applied to estimate the relevant information from the data after which high-dimensional feature space was reduced to a two-dimensional space by using the nonlinear Sammon’s map. The statistical method Non-Parametric Analysis of Variance was employed for the verification of relevant statistical differences among the groups (p < 0.05). In addition, K-fold cross-validation for discriminant analysis based on Gaussian Finite Mixture Modeling was employed for data classification. The results showed visual and statistical differences for all groups and conditions (i.e., static and dynamic tasks). The employed methods were successful for the discrimination of the groups. Classification accuracy was 81%±6% (mean ± standard deviation) and 71%±8%, for classification and test groups respectively. This research showed the discrimination between healthy and diseased groups conditions. The methods were also able to discriminate individuals with PD treated with DBS and levodopa. These methods enable objective characterization and visualization of features extracted from inertial and electromyographic sensors for different groups.