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
A treatment–response index from wearable sensors for quantifying Parkinson's disease motor states
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0003-0403-338X
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-3183-3756
Show others and affiliations
2017 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208Article in journal (Refereed) In press
Abstract [en]

The goal of this study was to develop an algorithm that automatically quantifies motor states (off,on,dyskinesia) in Parkinson's disease (PD), based on accelerometry during a hand pronation-supination test. Clinician's ratings using the Treatment Response Scale (TRS), ranging from -3 (very Off) to 0 (On) to +3 (very dyskinetic), was used as target. For that purpose, 19 participants with advanced PD and 22 healthy persons were recruited in a single center open label clinical trial in Uppsala, Sweden. The trial consisted of single levodopa dose experiments for the people with PD (PwP), where participants were asked to perform standardized wrist rotation tests, using each hand, before and at pre-specified time points after the dose. The participants used wrist sensors containing a 3D accelerometer and gyroscope. Features to quantify the level, variation and asymmetry of the sensor signals, three-level Discrete Wavelet Transform features and approximate entropy measures were extracted from the sensors data. At the time of the tests, the PwP were video recorded. Three movement disorder specialists rated the participants’ state on the TRS scale. A Treatment Response Index from Sensors (TRIS) was constructed to quantify the motor states based on the wrist rotation tests. Different machine learning algorithms were evaluated to map the features derived from the sensor data to the ratings provided by the three specialists. Results from cross validation, both in 10-fold and a leave-one-individual out setting, showed good predictive power of a support vector machine model and high correlation to the TRS scale. Values at the end tails of the TRS scale were under and over predicted due to the lack of observations at those values but the model managed to accurately capture the dose - effect profiles of the patients. In addition, the TRIS had good test-retest reliability on the baseline levels of the PD participants (Intraclass correlation coefficient of 0.83) and reasonable sensitivity to levodopa treatment (0.33 for the TRIS). For a series of test occasions the proposed algorithms provided dose - effect time profiles for participants with PD, which could be useful during therapy individualization of people suffering from advanced PD

Place, publisher, year, edition, pages
2017.
Keyword [en]
Accelerometers, Accelerometry, Diseases, Feature extraction, Levodopa response, Machine learning, Parkinson's disease, Pattern recognition, Sensor phenomena and characterization, Signal processing, Wearable sensors, Wrist
National Category
Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-26745DOI: 10.1109/JBHI.2017.2777926Scopus ID: 2-s2.0-85035809095OAI: oai:DiVA.org:du-26745DiVA: diva2:1164219
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Thomas, IliasWestin, JerkerAlam, MoududMemedi, Mevludin

Search in DiVA

By author/editor
Thomas, IliasWestin, JerkerAlam, MoududMemedi, Mevludin
By organisation
Microdata AnalysisComputer EngineeringStatistics
In the same journal
IEEE journal of biomedical and health informatics
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 22 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