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Machado ARP, Zaidan HC, Paixão APS, Cavalheiro GL, Oliveira FHM, Júnior JAFB, Naves K, Pereira AA, Pereira JM, Pouratian N, Zhuo X, O'Keeffe A, Sharim J, Bordelon Y, Yang L, Vieira MF, Andrade AO. Feature visualization and classification for the discrimination between individuals with Parkinson's disease under levodopa and DBS treatments. Biomed Eng Online 2016; 15:169. [PMID: 28038673 PMCID: PMC5203727 DOI: 10.1186/s12938-016-0290-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 11/26/2016] [Indexed: 12/15/2022] Open
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.
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Affiliation(s)
- Alessandro R P Machado
- 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.
| | - Hudson Capanema Zaidan
- 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
| | - Ana Paula Souza Paixão
- 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
| | - Guilherme Lopes Cavalheiro
- 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
| | - Fábio Henrique Monteiro Oliveira
- 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
| | - João Areis Ferreira Barbosa Júnior
- 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
| | - Kheline Naves
- 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
| | - Adriano Alves Pereira
- 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
| | | | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Xiaoyi Zhuo
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Andrew O'Keeffe
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Justin Sharim
- Department of Neurosurgery, University of California, Los Angeles, USA
| | - Yvette Bordelon
- Department of Neurology, University of California, Los Angeles, USA
| | - Laurice Yang
- Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Marcus Fraga Vieira
- Bioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil
| | - Adriano O Andrade
- 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
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