liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tensor Decomposition of Gait Dynamics in Parkinson's Disease
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
City University of Hong Kong, Hong Kong.
2018 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 65, no 8, p. 1820-1827Article in journal (Refereed) Published
Abstract [en]

Objective: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multi-channel or multi-sensor data analysis of gait dynamics in Parkinson's disease.

Method: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multi-sensor time series of gait force between Parkinson's disease and healthy control cohorts.

Results: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross-validations.

Conclusion: Tensor decomposition is a useful method for the modeling and analysis of multi-sensor time series in patients with Parkinson's disease.

Significance: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 65, no 8, p. 1820-1827
Keywords [en]
Parkinson's disease, gait dynamics, time series, multi-sensors, tensor decomposition, pattern classification, Tensile stress, Foot, Parkinson's disease, Databases, Time series analysis, Feature extraction, Force
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-143304DOI: 10.1109/TBME.2017.2779884ISI: 000439382300016Scopus ID: 2-s2.0-85037623890OAI: oai:DiVA.org:liu-143304DiVA, id: diva2:1162054
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2018-08-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Pham, Tuan
By organisation
Division of Biomedical EngineeringFaculty of Science & Engineering
In the same journal
IEEE Transactions on Biomedical Engineering
Other Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 86 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf