Feature visualization and classification for the discrimination between individuals with Parkinson's disease under levodopa and DBS treatments. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Machado, Alessandro; Zaidan, Hudson; Paixão, Ana; Cavalheiro, Guilherme; Oliveira, Fábio; Júnior, João; Naves, Kheline; Pereira, Adriano; Pereira, Janser; Pouratian, Nader; Zhuo, Xiaoyi; O'Keeffe, Andrew; Sharim, Justin; Bordelon, Yvette; Yang, Laurice; Vieira, Marcus; Andrade, Adriano
• Is Part Of: Biomedical engineering online. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 22
• Publication Date: 2016-12
• Subjects: Parkinson's disease -- Deep brain stimulation -- Levodopa -- Inertial sensors -- Electromyography -- Discriminant analysis; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-016-0290-y
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background 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. Methods 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 (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 the high-dimensional feature space was reduced to a two-dimensional space using the nonlinear Sammon's map. 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. Results 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 training and test groups respectively. Conclusions 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.