du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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
Feasibility of spirography features for objective assessment of motor symptoms in Parkinson's disease
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, Slovenia .
Show others and affiliations
2015 (English)In: Artificial Intelligence in Medicine: 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings / [ed] John Holmes, Riccardo Bellazzi, Lucia Sacchi and Niels Peek, Springer, 2015, Vol. 9105, 267-276 p.Conference paper, Published paper (Refereed)
Abstract [en]

Parkinsons disease (PD) is currently incurable, however the proper treatment can ease the symptoms and significantly improve the quality of patients life. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper is to investigate the feasibility of using the features and methodology of a spirography device, originally designed to measure early Parkinsons disease (PD) symptoms, for assessing motor symptoms of advanced PD patients suffering from motor fluctuations. More specifically, the aim is to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). The work combines spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The target outcome was to learn to predict the “cause” of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. Using the machine learning methods with feature descriptions from the Slovenian application resulted in 86% classification accuracy and over 90% AUC, demonstrating the usefulness of this approach for objective monitoring of PD patients.

Place, publisher, year, edition, pages
Springer, 2015. Vol. 9105, 267-276 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9105
Keyword [en]
Parkinson's disease, movement disorder, spirography, spirography features, objective monitoring
National Category
Computer Engineering Information Systems
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
Identifiers
URN: urn:nbn:se:du-19015DOI: 10.1007/978-3-319-19551-3_35ISBN: 978-3-319-19550-6 (print)ISBN: 978-3-319-19551-3 (electronic)OAI: oai:DiVA.org:du-19015DiVA: diva2:846076
Conference
15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015.
Projects
FLOAT - Flexibel levodopa-optimerings och individanpassningsteknik
Funder
Knowledge Foundation
Available from: 2015-08-14 Created: 2015-08-14 Last updated: 2017-03-30Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Memedi, Mevludin
By organisation
Computer Engineering
Computer EngineeringInformation Systems

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 417 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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