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Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia

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Zhao, Z, Fang, H, Williams, S, Relton, SD, Alty, J ORCID: 0000-0002-5456-8676, Casson, AJ and Wong, DC 2020 , 'Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia', in 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) , Institute of Electrical and Electronics Engineers, Montreal, QC, Canada, pp. 780-783 , doi: 10.1109/EMBC44109.2020.9175638.

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Abstract

Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no bradykinesia and participants with severe bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— Our work visually presents the archetypal types of bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no bradykinesia, and the other showing linear decrement over time.

Item Type: Conference Publication
Authors/Creators:Zhao, Z and Fang, H and Williams, S and Relton, SD and Alty, J and Casson, AJ and Wong, DC
Keywords: computer vision, artificial intelligence, Parkinson's
Journal or Publication Title: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publisher: Institute of Electrical and Electronics Engineers
DOI / ID Number: 10.1109/EMBC44109.2020.9175638
Copyright Information:

Copyright 2020 IEEE

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