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Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease
Dalarna University, School of Technology and Business Studies, Computer Engineering.ORCID iD: 0000-0002-2372-4226
Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
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2015 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 9, 23727-23744 p.Article in journal (Refereed) Published
Abstract [en]

A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Place, publisher, year, edition, pages
MDPI , 2015. Vol. 15, no 9, 23727-23744 p.
Keyword [en]
bradykinesia; digital spiral analysis; dyskinesia; machine learning; motor fluctuations; objective measures; Parkinson’s disease; remote monitoring; time series analysis; visualization
National Category
Computer Engineering Computer Science Information Systems
Research subject
Complex Systems – Microdata Analysis, FLOAT - Flexible Levodopa Optimizing Assistive Technology
URN: urn:nbn:se:du-19472DOI: 10.3390/s150923727ISI: 000362512200139PubMedID: 26393595OAI: diva2:854673
Knowledge Foundation
Available from: 2015-09-17 Created: 2015-09-17 Last updated: 2016-04-14Bibliographically approved

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Memedi, Mevludin
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