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Feasibility of Using Dynamic Time Warping to Measure Motor States in Parkinson’s Disease
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: 0000-0002-1429-2345
Halmstad University.
2020 (English)In: Journal of Sensors, ISSN 1687-725X, E-ISSN 1687-7268, Vol. 2020, article id 3265795Article in journal (Refereed) Published
Abstract [en]

The aim of this paper is to investigate the feasibility of using the Dynamic Time Warping (DTW) method to measure motor states in advanced Parkinson’s disease (PD). Data were collected from 19 PD patients who experimented leg agility motor tests with motion sensors on their ankles once before and multiple times after an administration of 150% of their normal daily dose of medication. Experiments of 22 healthy controls were included. Three movement disorder specialists rated the motor states of the patients according to Treatment Response Scale (TRS) using recorded videos of the experiments. A DTW-based motor state distance score (DDS) was constructed using the acceleration and gyroscope signals collected during leg agility motor tests. Mean DDS showed similar trends to mean TRS scores across the test occasions. Mean DDS was able to differentiate between PD patients at Off and On motor states. DDS was able to classify the motor state changes with good accuracy (82%). The PD patients who showed more response to medication were selected using the TRS scale, and the most related DTW-based features to their TRS scores were investigated. There were individual DTW-based features identified for each patient. In conclusion, the DTW method can provide information about motor states of advanced PD patients which can be used in the development of methods for automatic motor scoring of PD.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2020. Vol. 2020, article id 3265795
Keywords [en]
Dynamic Time Warping, Parkinson's disease, signal processing
National Category
Medical Engineering
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-31909DOI: 10.1155/2020/3265795ISI: 000522323600001Scopus ID: 2-s2.0-85082725240OAI: oai:DiVA.org:du-31909DiVA, id: diva2:1393398
Available from: 2020-02-16 Created: 2020-02-16 Last updated: 2020-05-05Bibliographically 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, SomayehFleyeh, HasanDougherty, Mark

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