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Motion sensor-based assessment of Parkinson’s disease motor symptoms during leg agility tests: results from levodopa challenge
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-1548-5077
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2020 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 1, p. 111-119, article id 8637809Article in journal (Refereed) Published
Abstract [en]

Parkinson’s disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

Place, publisher, year, edition, pages
IEEE, 2020. Vol. 24, no 1, p. 111-119, article id 8637809
National Category
Physiotherapy Other Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-29542DOI: 10.1109/JBHI.2019.2898332ISI: 000506642000012Scopus ID: 2-s2.0-85077669455OAI: oai:DiVA.org:du-29542DiVA, id: diva2:1290650
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2020-03-18Bibliographically approved
In thesis
1. Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
Open this publication in new window or tab >>Sensor-based knowledge- and data-driven methods: A case of Parkinson’s disease motor symptoms quantification
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The overall aim of this thesis was to develop and evaluate new knowledge- and data-driven methods for supporting treatment and providing information for better assessment of Parkinson’s disease (PD).

PD is complex and progressive. There is a large amount of inter- and intravariability in motor symptoms of patients with PD (PwPD). The current evaluation of motor symptoms that are done at clinics by using clinical rating scales is limited and provides only part of the health status of PwPD. An accurate and clinically approved assessment of PD is required using frequent evaluation of symptoms.

To investigate the problem areas, the thesis adopted the microdata analysis approach including the stages of data collection, data processing, data analysis, and data interpretation. Sensor systems including smartphone and tri-axial motion sensors were used to collect data from advanced PwPD experimenting with repeated tests during a day. The experiments were rated by clinical experts. The data from sensors and the clinical evaluations were processed and used in subsequent analysis.

The first three papers in this thesis report the results from the investigation, verification, and development of knowledge- and data-driven methods for quantifying the dexterity in PD. The smartphone-based data collected from spiral drawing and alternate tapping tests were used for the analysis. The results from the development of a smartphone-based data-driven method can be used for measuring treatment-related changes in PwPD. Results from investigation and verification of an approximate entropy-based method showed good responsiveness and test-retest reliability indicating that this method is useful in measuring upper limb temporal irregularity.

The next two papers, report the results from the investigation and development of motion sensor-based knowledge- and data-driven methods for quantification of the motor states in PD. The motion data were collected from experiments such as leg agility, walking, and rapid alternating movements of hands. High convergence validity resulted from using motion sensors during leg agility tests. The results of the fusion of sensor data gathered during multiple motor tests were promising and led to valid, reliable and responsive objective measures of PD motor symptoms.

Results in the last paper investigating the feasibility of using the Dynamic Time-Warping method for assessment of PD motor states showed it is feasible to use this method for extracting features to be used in automatic scoring of PD motor states.

The findings from the knowledge- and data-driven methodology in this thesis can be used in the development of systems for follow up of the effects of treatment and individualized treatments in PD.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2020
Series
Dalarna Doctoral Dissertations ; 12
Keywords
Parkinson’s disease, motion sensors, motor symptoms, smartphone, microdata, multivariate analysis, data-driven, knowledge-driven, support vector machine stepwise regression, predictive models
National Category
Computer Systems Computer Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-32065 (URN)978-91-88679-00-0 (ISBN)
Public defence
2020-05-08, Clas Ohlson, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2020-04-15 Created: 2020-02-26 Last updated: 2020-04-15Bibliographically approved

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Aghanavesi, Somayeh

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