Evidence map›Paper›PMID 28038673›Full record

ArticleBiomedical engineering online2016

Feature visualization and classification for the discrimination between individuals with Parkinson's disease under levodopa and DBS treatments.

Alessandro R P Machado, Hudson Capanema Zaidan, Ana Paula Souza Paixão, Guilherme Lopes Cavalheiro, Fábio Henrique Monteiro Oliveira, João Areis Ferreira Barbosa Júnior, Kheline Naves, Adriano Alves Pereira, Janser Moura Pereira, Nader Pouratian and 7 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06536348 (PARKINSON BRASIL - Database on Motor and Non-Motor Aspects of Parkinson's Disease in Brazil), which is not on this map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06536348 recruitingnot on this mapstarted 2025, after this paper: background citation

PARKINSON BRASIL - Database on Motor and Non-Motor Aspects of Parkinson's Disease in Brazil

TypeobservationalSponsorFederal University of UberlandiaRan2025 to 2034Enrolled958ConditionsParkinson Disease
3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. PeerJ. Computer science · 2023
    Article
  3. Article
  4. Review
  5. Article
  6. On the Use ofComputational and mathematical methods in medicine · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Alessandro R P MachadoPostgraduate 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.ORCID http://orcid.org/0000-0003-4581-9354
Hudson Capanema ZaidanPostgraduate 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 PaixãoPostgraduate 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 CavalheiroPostgraduate 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 OliveiraPostgraduate 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 JúniorPostgraduate 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 NavesPostgraduate 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 PereiraPostgraduate 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.
Janser Moura PereiraFaculty of Mathematics, Federal University of Uberlândia, Uberlândia, Brazil.
Nader PouratianDepartment of Neurosurgery, University of California, Los Angeles, USA.
Xiaoyi ZhuoDepartment of Neurosurgery, University of California, Los Angeles, USA.
Andrew O'KeeffeDepartment of Neurosurgery, University of California, Los Angeles, USA.
Justin SharimDepartment of Neurosurgery, University of California, Los Angeles, USA.
Yvette BordelonDepartment of Neurology, University of California, Los Angeles, USA.
Laurice YangNeurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
Marcus Fraga VieiraBioengineering and Biomechanics Laboratory, Federal University of Goiás, Goiânia, Brazil.
Adriano O AndradePostgraduate 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOver 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.

methodsIn 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Deep Brain StimulationAgedAged, 80 and overCase-Control StudiesElectromyographyFemaleHumansLevodopaMaleMiddle AgedParkinson DiseaseTreatment OutcomeLevodopaDeep brain stimulationDiscriminant analysisElectromyographyInertial sensorsLevodopaParkinson’s disease

Identifiers

PMID28038673
PMCPMC5203727

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.