uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • 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 function in Parkinson's disease
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana, Slovenia..
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana, Slovenia..
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana, Slovenia..
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, Ljubljana, Slovenia..
Show others and affiliations
2017 (English)In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 81, no SI, p. 54-62Article in journal (Refereed) Published
Abstract [en]

Objective: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was 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).

Materials and methods: This work combined 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 study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification.

Results: Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases.

Conclusions: The relatively high classification accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

Place, publisher, year, edition, pages
2017. Vol. 81, no SI, p. 54-62
Keywords [en]
Parkinson's disease, Movement disorder, Spirography, Spirography features, Objective monitoring, Visualisation
National Category
Neurology
Identifiers
URN: urn:nbn:se:uu:diva-340159DOI: 10.1016/j.artmed.2017.03.011ISI: 000413608900006PubMedID: 28416144OAI: oai:DiVA.org:uu-340159DiVA, id: diva2:1179540
Conference
15th Conference on Artificial Intelligence in Medicine (AIME), JUN 17-20, 2015, Univ Pavia, Pavia, ITALY
Funder
VINNOVAAvailable from: 2018-02-01 Created: 2018-02-01 Last updated: 2018-02-01Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Authority records BETA

Nyholm, Dag

Search in DiVA

By author/editor
Nyholm, Dag
By organisation
Neurology
In the same journal
Artificial Intelligence in Medicine
Neurology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • 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