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Verification of a method for measuring Parkinson’s disease related temporal irregularity in spiral drawings
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0002-1548-5077
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-2372-4226
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed) Published
Abstract [en]

Parkinson's disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

Place, publisher, year, edition, pages
2017. Vol. 17, no 10, article id 2341
Keywords [en]
Parkinson's disease; smartphone; spiral tests; temporal irregularity; timing variability; motor assessment; approximate entropy; complexity
National Category
Other Medical Engineering Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-29318DOI: 10.3390/s17102341ISI: 000414931500183PubMedID: 29027941Scopus ID: 2-s2.0-85032855199OAI: oai:DiVA.org:du-29318DiVA, id: diva2:1280955
Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2020-05-12Bibliographically 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, SomayehMemedi, MevludinDougherty, MarkWestin, Jerker

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