title: Feature visualization and classification for the discrimination between individuals with Parkinson's disease under levodopa and DBS treatments.
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.
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil. alessandrorpmachado@yahoo.com.br.
authors.forename: Alessandro R P
authors.identifier: http://orcid.org/0000-0003-4581-9354
authors.initials: AR
authors.lastname: Machado
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Hudson Capanema
authors.initials: HC
authors.lastname: Zaidan
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Ana Paula Souza
authors.initials: AP
authors.lastname: Paixão
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Guilherme Lopes
authors.initials: GL
authors.lastname: Cavalheiro
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Fábio Henrique Monteiro
authors.initials: FH
authors.lastname: Oliveira
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: João Areis Ferreira Barbosa
authors.initials: JA
authors.lastname: Júnior
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Kheline
authors.initials: K
authors.lastname: Naves
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Adriano Alves
authors.initials: AA
authors.lastname: Pereira
authors.affiliation: Faculty of Mathematics, Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Janser Moura
authors.initials: JM
authors.lastname: Pereira
authors.affiliation: Department of Neurosurgery, University of California, Los Angeles, USA.
authors.forename: Nader
authors.initials: N
authors.lastname: Pouratian
authors.affiliation: Department of Neurosurgery, University of California, Los Angeles, USA.
authors.forename: Xiaoyi
authors.initials: X
authors.lastname: Zhuo
authors.affiliation: Department of Neurosurgery, University of California, Los Angeles, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: O'Keeffe
authors.affiliation: Department of Neurosurgery, University of California, Los Angeles, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Sharim
authors.affiliation: Department of Neurology, University of California, Los Angeles, USA.
authors.forename: Yvette
authors.initials: Y
authors.lastname: Bordelon
authors.affiliation: Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
authors.forename: Laurice
authors.initials: L
authors.lastname: Yang
authors.affiliation: Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil.
authors.forename: Marcus Fraga
authors.initials: MF
authors.lastname: Vieira
authors.affiliation: Postgraduate Program in Electrical and Biomedical Engineering, Faculty of Electrical Engineering, Centre for Innovation and Technology Assessment in Health (NIATS), Federal University of Uberlândia, Uberlândia, Brazil.
authors.forename: Adriano O
authors.initials: AO
authors.lastname: Andrade
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D046690:Deep Brain Stimulation; D004576:Electromyography; D005260:Female; D006801:Humans; D007980:Levodopa / Q000627:therapeutic use*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000188:drug therapy / Q000503:physiopathology / Q000628:therapy*; D016896:Treatment Outcome
chemical_list: D007980:Levodopa
keywords: Deep brain stimulation; Discriminant analysis; Electromyography; Inertial sensors; Levodopa; Parkinson’s disease